2020 Labor Market and Economic Report - Wa

Employment Security Department WASHINGTON STATE

2020 LABOR MARKET AND ECONOMIC REPORT

U.S. economy

Washington’s economy

Seasonal employment

Unemployment

Employment projections

Income

Economic comparisons

Labor Market and Economic Analysis

March 2021

2020 Labor Market and Economic Report Published March 2021

Washington State Employment Security Department Cami Feek, acting commissioner

Policy, Data, Performance and Integrity Dan Zeitlin, director

Labor Market and Economic Analysis Steven Ross, director of labor market information

Report content is based on data available through September 2019.

Report authors:

Fast facts: Robert Haglund, operations management analyst Executive summary: Paul Turek, state labor economist Chapter 1: Paul Turek, state labor economist Chapter 2: Paul Turek, state labor economist Chapter 3: Josh Moll, econometric and actuarial analysis manager Chapter 4: Jef Robinson, econometric and actuarial analysis manager Chapter 5: Josh Moll, econometric and actuarial analysis manager Bruce Nimmo, economic analyst Chapter 6: Anneliese Vance-Sherman, regional labor economist Chapter 7: Robert Haglund, operations management analyst Report production: Sandra K. Jones, operations management analyst Project management: Pierre Bell, operations management analyst

Tis workforce product was funded by a grant awarded by the U.S. Department of Labor’s Employment and Training Administration. Te product was created by the recipient and does not necessarily refect the ofcial position of the U.S. Department of Labor. Te Department of Labor makes no guarantees, warranties, or assurances of any kind, expressed or implied, with respect to such information, including any information on linked sites and including, but not limited to, accuracy of the information or its completeness, timeliness, usefulness, adequacy, continued availability, or ownership. Tis product is copyrighted by the institution that created it. Internal use by an organization and/or personal use by an individual for non-commercial purposes is permissible. All other uses require the prior authorization of the copyright owner.

Tis report can be viewed online and downloaded at esd.wa.gov/labormarketinfo/annual-report.

Further analysis and detailed statistics are available from the Employment Security Department upon request. Historical values are subject to revision and may not equal prior report values. To get this report in an alternative format, call the Labor Market and Economic Analysis Division at 833-572-8421.

Employment Security Department is an equal opportunity employer/program. Auxiliary aids and services are available upon request to individuals with disabilities. Language assistance services for limited English profcient individuals are available free of charge. Washington Relay Service: 711.

https://esd.wa.gov/labormarketinfo/annual-report

Contents Labor market fast facts...................................................................................................................................................... iii

Executive summary ............................................................................................................................................................ v

Chapter 1: U.S. economy and labor market ................................................................................................................. 1

Chapter 2: Washington’s economy and labor market ................................................................................................ 25

Chapter 3: Seasonal, structural and cyclical industry employment......................................................................... 39

Chapter 4: Unemployment.............................................................................................................................................. 45

Chapter 5: Employment projections ............................................................................................................................. 57

Chapter 6: Income ............................................................................................................................................................. 71

Chapter 7: Economic comparisons with other states................................................................................................. 81

Appendices .......................................................................................................................................................................... 89

Appendix 1: Washington’s workforce development areas................................................................................... 89

Appendix 2: Seasonal, structural and cyclical industry employment ............................................................... 91

Appendix 3: Use and misuse of employment projections................................................................................... 101

Appendix 4: Occupations in Demand (OID) methodology............................................................................. 103

Appendix 5: Skills projections .................................................................................................................................. 107

Appendix 6: Frequently asked questions ................................................................................................................ 113

Appendix 7: Glossary of terms.................................................................................................................................. 115

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Labor market fast facts Fast facts 1. Labor force and unemployment, not seasonally adjusted Washington state, annual data from January 2000 to September 2020 Source: Employment Security Department/LMEA; U.S. Bureau of Labor Statistics, Local Area Unemployment Statistics

Year Labor force Employed Unemployed Unemployment rate 2000 3,059,339 2,901,492 157,847 5.2% 2005 3,263,703 3,082,399 181,304 5.6% 2006 3,323,938 3,156,626 167,312 5.0% 2007 3,403,163 3,243,308 159,855 4.7% 2008 3,478,577 3,291,309 187,268 5.4% 2009 3,535,200 3,211,649 323,551 9.2% 2010 3,511,326 3,160,544 350,782 10.0% 2011 3,461,428 3,140,190 321,238 9.3% 2012 3,471,282 3,189,271 282,011 8.1% 2013 3,463,869 3,219,842 244,027 7.0% 2014 3,489,666 3,275,753 213,913 6.1% 2015 3,545,904 3,345,496 200,408 5.7% 2016 3,635,200 3,444,126 191,074 5.3% 2017 3,724,722 3,547,430 177,292 4.8% 2018 3,793,095 3,622,299 170,796 4.5% 2019 3,914,154 3,747,713 166,441 4.3% 2020 January to September* 3,943,199 3,590,499 352,700 8.9%

*2020 data is averaged for nine months.

Fast facts 2. Labor force and unemployment, not seasonally adjusted Washington state metropolitan areas, January to September 2020 Source: Employment Security Department/LMEA; U.S. Bureau of Labor Statistics, Local Area Unemployment Statistics

Metropolitan area Labor force Employed Unemployed Unemployment rate Washington state 3,906,624 3,617,002 289,622 7.4% Bellingham 116,057 106,741 9,316 8.0% Bremerton 130,022 121,241 8,781 6.8% Kennewick-Pasco-Richland 156,898 146,726 10,172 6.5% Longview-Kelso 48,459 44,367 4,092 8.4% Mount Vernon-Anacortes 62,287 57,175 5,112 8.2% Olympia 142,205 132,063 10,142 7.1% Seattle-Bellevue-Everett MD* 1,718,462 1,596,474 121,988 7.1% Spokane 269,397 248,626 20,771 7.7% Tacoma MD * (Pierce) 444,348 406,252 38,096 8.6% Wenatchee 69,933 65,653 4,280 6.1% Yakima 140,742 130,636 10,106 7.2%

*Metropolitan Division

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Fast facts 3. Projected industry average annual employment growth rates Washington state, 2018 to 2028 Source: Employment Security Department/LMEA; U.S. Bureau of Labor Statistics

NAICS Industry sector 2019 Q2 to 2021 Q2 2018 to 2023 2023 to 2028 Total nonfarm 1.32% 1.56% 1.17%

22, 48, 49 Transportation, warehousing and utilities 1.39% 1.08% 0.82% 23 Construction 0.14% 0.95% 0.39% 31-33 Manufacturing 0.43% 0.69% 0.35% 42 Wholesale trade 0.55% 0.48% 0.51% 44-45 Retail trade 1.05% 1.04% 1.15% 51 Information 3.48% 3.85% 1.74% 52 Financial activities 1.09% 1.03% 0.66% 54-56 Professional and business services 1.81% 1.72% 1.40% 61-62 Education and health services 1.75% 2.13% 2.09% 71-72 Leisure and hospitality 1.79% 1.39% 1.23% GOV Government 1.11% 1.08% 1.08%

Fast facts 4. Wages and employment by industry Washington state, 2019 annual averages (revised) Source: Employment Security Department/LMEA; U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages

NAICS Industry sector Average

number of firms Total

wages paid Average

employment Average

weekly wage Total 228,397 $239,305,634,313 3,437,987 $1,339

11 Agriculture, forestry, fishing and hunting 6,845 $3,471,899,226 103,017 $648 21 Mining 136 $168,260,156 2,248 $1,439 22 Utilities 231 $544,796,308 5,174 $2,025 23 Construction 26,254 $13,943,150,382 205,619 $1,304 31 - 33 Manufacturing 7,510 $23,577,121,022 290,238 $1,562 42 Wholesale trade 12,484 $11,024,925,274 133,790 $1,585 44 - 45 Retail trade 14,062 $23,940,081,411 384,491 $1,197 48 - 49 Transportation and warehousing 4,628 $6,789,580,684 104,925 $1,244 51 Information 4,402 $29,788,858,141 143,836 $3,983 52 Finance and insurance 5,875 $9,567,218,584 94,774 $1,941 53 Real estate, rental and leasing 6,909 $3,233,756,895 55,354 $1,123 54 Professional, scientific and technical services 26,501 $21,669,812,834 208,493 $1,999 55 Management of companies and enterprises 651 $5,587,879,141 45,243 $2,375 56 Administrative and waste management services 12,383 $9,119,844,305 171,641 $1,022 61 Educational services 3,432 $1,820,149,250 45,251 $774 62 Healthcare and social assistance 57,546 $23,651,556,863 432,809 $1,051 71 Arts, entertainment and recreation 2,894 $1,782,541,572 53,789 $637 72 Accommodation and food services 14,727 $7,277,988,322 287,434 $487 81 Other services (except public administration) 18,801 $4,366,351,021 102,534 $819 GOV Government 2,128 $37,979,862,922 567,327 $1,287

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Fast facts 5. Percent of households by income range, 2019 dollars Washington state, 2015 through 2019 Source: U.S. Census Bureau, American Community Survey

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20% 18% 16% 14% 12% 10%

8% 6% 4% 2% 0%

2015 2016 2017 2018 2019

<$10K $10K- $15K- $25K- $35K- $50K- $75K- $100K- $150K- $200K+ $15K $25K $35K $50K $75K $100K $150K $200K

Household income range in 2019 dollars

I I I I I I I I I ----------Fast facts 6. Contributions to percent change in real GDP, seasonally adjusted annualized rate United States, third quarter 2018 through third quarter 2020 Source: U.S. Bureau of Economic Analysis, Domestic Product and Income

Contributions 2018 Q3 2018 Q4 2019 Q1 2019 Q2 2019 Q3 2019 Q4 2020 Q1 2020 Q2 2020 Q3 GDP percent change annual rate 2.1 1.3 2.9 1.5 2.6 2.4 -5.0 -31.4 33.1 Percentage contribution by factor Consumption expenditures 1.79 1.05 1.25 2.47 1.83 1.07 -4.75 -24.01 25.27 Fixed investment 0.14 0.46 0.50 -0.07 0.42 0.17 -0.27 -5.27 4.96 Change in private inventories 1.58 0.23 0.21 -0.97 -0.09 -0.82 -1.34 -3.50 6.62 Net exports of goods and services -1.83 -0.27 0.55 -0.79 0.04 1.52 1.13 0.62 -3.09 Government expenditures 0.44 -0.16 0.43 0.86 0.37 0.42 0.22 0.77 -0.68

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Fast facts 7. Highest and lowest state unemployment rates, not seasonally adjusted, based on 2019 ranking United States and Washington state, 2009, 2014 and 2019 Source: U.S. Bureau of Labor Statistics, Local Area Unemployment Statistics

Rank State 2009 2014 2019 United States 9.3% 6.2% 3.7%

1 North Dakota 4.1% 2.7% 2.4% 1 Vermont 6.6% 3.9% 2.4% 3 New Hampshire 6.2% 4.3% 2.5% 4 Utah 7.3% 3.8% 2.6% 5 Hawaii 7.2% 4.4% 2.7% 5 Iowa 6.4% 4.2% 2.7% 7 Colorado 7.3% 5.0% 2.8% 7 South Carolina 11.2% 6.5% 2.8% 7 Virginia 6.7% 5.2% 2.8% 10 Idaho 8.8% 4.8% 2.9% 10 Massachusetts 8.1% 5.7% 2.9% 37 California 11.2% 7.5% 4.0% 37 Illinois 10.2% 7.1% 4.0% 37 New York 8.3% 6.3% 4.0% 40 Michigan 13.7% 7.2% 4.1% 40 Ohio 10.3% 5.8% 4.1% 42 Kentucky 10.3% 6.5% 4.3% 42 Washington 9.2% 6.1% 4.3% 44 Pennsylvania 8.0% 5.9% 4.4% 45 Arizona 9.9% 6.8% 4.7% 46 Louisiana 6.8% 6.4% 4.8% 47 New Mexico 7.5% 6.7% 4.9% 47 West Virginia 7.7% 6.6% 4.9% 49 Mississippi 9.5% 7.5% 5.4% 50 District of Columbia 9.3% 7.8% 5.5% 51 Alaska 7.7% 6.9% 6.1%

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Executive summary U.S. economy and labor market Te U.S. economy entered 2020 with strong momentum. Te unemployment rate was on its way to reaching a 50-year low of 3.5 percent in February 2020. Te economic expansion, which had been in place since July 2009, appeared poised to run for at least another year.

Te emergence and spread of the COVID-19 virus in early 2020 created a world-wide pandemic. Policymakers quickly responded to the crisis by issuing stay-at-home orders and promoting social distancing guidelines. Many businesses that relied on personal contact with customers shut down or drastically scaled back their operations. Te abrupt decline in economic activity brought to a halt the period of economic expansion following the Great Recession. Economic growth turned sharply negative before rebounding in the third quarter, prompting the National Bureau of Economic Research to declare that the U.S. economy was in recession beginning in March 2020.

Total nonfarm employment in the United States fell by 1.37 million in March and 20.8 million in April before rebounding to gain 2.7 million in May. Employers added jobs for the ffh consecutive month in September, bringing the total number of jobs recovered from the virus-related low to 11.5 million. Tis represents just over half of the jobs that were lost in March and April.

Job losses since February 2020 have been widespread across industries. Te leisure and hospitality industry, which includes restaurants and bars, hotels, and entertainment and recreation, lost almost half of its jobs during the shutdown period. As the economy reopened, it gained back a good many of lost jobs, but still ranks as the industry sufering the most losses.

Washington’s economy and labor market Washington’s economy was very strong for most of the economic expansion that took place afer the Great Recession. Te COVID-19 outbreak in early 2020 altered the state’s economic good fortune and put its resiliency to the test. Washington was one of the frst states to sufer an outbreak and one of the frst to put in place aggressive mitigation eforts. Tose eforts caused the level of economic activity in the state to sharply pull back. Massive job losses resulted in March through May and the state’s unemployment rate spiked upward.

Employment bounced back in June with enough forward momentum to carry strong gains into July and August. To date in October 2020, the state has been able to recapture about 60 percent of the jobs lost from March through May. As in most states, leisure and hospitality accounted for the vast majority of job losses.

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Since the economy has yet to fully recover, unemployment rates remain elevated. Te unemployment rate for the U.S. dropped to 6.9 percent in October 2020 afer having reached a high of 14.7 percent in April. Washington’s rate has come back down to 6.0 percent, below the national rate. Seattle had an unemployment rate of 4.8 percent in October. In contrast, the unemployment rate for the balance of the state, minus the Seattle-Belleville-Everett area, was 6.8 percent.

Seasonal, structural and cyclical industry employment An analysis of 95 industries in Washington state identifed 18 as having high levels of seasonality. Te analysis is based on historical data from January 2002 through December 2019. Te industries that are most sensitive to seasonal forces include crop production, scenic and sightseeing transportation, and support activities for agriculture and forestry. Tere were 29 industries that are most infuenced by structural factors. Structural factors such as productivity improvement, policy changes, technological innovation and social change have heavily infuenced employment in ambulatory healthcare services, local government, educational services, and beverage and tobacco product manufacturing. For 15 industries, the cyclical component accounts for more than half of the change in employment. Tose most infuenced by cyclical factors include support activities for mining, oil and gas extraction and scenic and sightseeing transportation.

Unemployment Te number of benefciaries in 2020 increased signifcantly starting in March 2020 with the COVID-19 pandemic, with the number of paid claims increasing by nearly three times the number of paid claims in April 2020. Te number of claimants receiving benefts peaked at 711,945 claimants in May 2020. For comparison, during the height of the great recession, January 2010, 305,086 people had received unemployment benefts.

Te level of exhaustions of regular claims has been increasing steadily since March 2020 with substantial increases in the number of exhaustions of regular unemployment benefts occurring in September 2020 (60,158 regular beneft exhaustions). From October 2019 through September 2020, workers in the arts, entertainment and recreation and the educational service sectors were most likely to exhaust regular unemployment benefts with an exhaustion-to-employment ratio of 8.9 percent and 8.7 percent respectively. Te accommodation and food services sector accounted for the greatest portion of regular beneft exhaustions at 18.9 percent.

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Employment projections Te 10-year average annual growth rate for total nonfarm employment for the 2018 to 2028 period is projected to be 1.37 percent. Tis is a decrease from the 1.51 percent average annual growth rate predicted last year for 2017 to 2027. Te largest increases by share of employment is projected for the information sector and other services sector. Te largest employment shares in 2028, from largest to smallest, are projected for the ofce and administrative support occupations, sales and related occupations and food preparation and serving-related occupations. As was the case in last year’s projections report, the frst two occupational groups are projected to have declining employment shares.

Income Recently released data show the median household income measured in 2019 dollars in Washington rose by 14.5 percent from 2015 to 2019. Te median Washington household income expanded more quickly than the median national household income, which grew by 9.9 percent over the same time period. As of 2019, Washington’s average household earnings were $107,023, a statistically signifcant increase of 3.7 percent over the previous year. From 2018 to 2019, median earnings increased from $40,559 to $41,735, an increase of $1,176 or 3 percent. Households with income ranges less than $35,000 accounted for about 24 percent of all households in 2015. By 2019, the share was closer to 21 percent. Households with incomes exceeding $100,000 per year expanded from about 32 percent in 2015 to 39 percent in 2019. Comparing median earnings for male versus female full-time/year-round workers reveals a persistent earnings gap. Women’s median earnings ($50,612 in 2019) are 79 percent of men’s ($63,988).

Economic comparisons with other states Tis chapter presents several tables of economic data, comparing Washington to the nation as a whole as well as other states and the District of Columbia. Minimum wage, unemployment rate, job growth, annual exports, per capita income, privately owned building permits and median single-family home costs are presented as economic indicators for comparison as well as a current ranking for Washington state.

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Chapter 1: U.S. economy and labor market Te U.S. economy entered 2020 with strong momentum. Gross Domestic Product (GDP) increased in every quarter of 2019 despite the uncertainty cast by trade disputes with China. Monetary policy had shifed to becoming accommodative to the economy. Job growth had been steady. Te labor market continued to tighten, but not enough to put upward pressure on earnings and threaten infation. Te unemployment rate was on its way to reaching a 50-year low of 3.5 percent in February 2020. A revised North American trade deal, the U.S.-Mexico-Canada Agreement (USMCA), was signed into law in January. During the same month, a new Phase I trade deal was reached with China. Trade tensions appeared to be cooling. Te economic expansion, which had been in place since July 2009, appeared poised to run for at least another year.

It wasn’t long before a new threat to the economy as well as to world health emerged. A new coronavirus, now known as COVID-19, was identifed in China in December 2019 and quickly spread throughout the world. Te World Health Organization declared the COVID-19 outbreak a public health emergency of international concern on January 30, 2020, and a pandemic on March 11, 2020.

A Washington state man became the frst confrmed domestic case in mid-January. On February 29, Governor Jay Inslee declared a state of emergency for the state of Washington to contain the spread of the virus. Soon, other states that had become infected began to follow suit. States and metropolitan areas, which contain more than three-quarters of the nation’s population, quickly implemented stay-at-home policies in March and promoted social distancing. Businesses that most typically relied on onsite group gatherings of customers and consumer spending either shut down or severely curtailed their business operations. Sports venues closed, along with most restaurants, movie theaters, and other forms of entertainment.

Millions of jobs were lost in the leisure and hospitality, healthcare, household and personal services and retailing sectors. Without income from employment, and without many places to spend or reason to frequent businesses, consumer spending plummeted. Economic activity retreated and economic growth came to an abrupt halt. To no real surprise, the National Bureau of Economic Research declared that economic activity in the U.S. had peaked in February and that a recession had begun in March.

Policymakers have responded to the economic fallout from the pandemic through both sizable fscal and monetary measures. Under normal circumstances these might have jolted the economy toward

Chapter 1 U.S. economy and labor market

recovery sooner rather than later. But this recession is unique in that it results from the extensive shutdown in economic activity put in place to contain the COVID-19 outbreak rather than a buildup of cyclical excesses or “bubbles.” As such, the U.S. economic outlook continues to be very dependent on the evolution of the pandemic. As of this writing, COVID-19 cases have begun to re-escalate and could lead to a renewal of lockdowns. On the other hand, news of efective vaccines developed by Pfzer and Moderna respectively create optimism for the near future when the vaccine can be deployed.

Recent changes in GDP GDP measures the value of the output of goods and services produced by the economy. A goal of the economy is for GDP to grow over time to increase the stock of products available to domestic households, as well as their ability to purchase them. As such, changes in real GDP are used as a measure of economic growth.

GDP grew at a slower pace overall in 2019 compared to the brisk pace it exhibited in 2018. Te economy had expanded at an annual rate of 3.0 percent in 2018, owing much of the boost to growth to tax code changes that helped to elevate household and business spending, and to increased levels of government spending. Te Tax Cuts and Jobs Act, which was signed into law in late 2017, helped to raise real disposable income, which in turn supported strong growth in real personal spending. Te 3.6 percent increase in real disposable income in 2018 was the strongest increase since 2015.

On the business side, corporate tax reform boosted corporate afer-tax proft and led to an acceleration in capital spending. Business investment spending increased by 6.3 percent in 2018, which accounted for 1.8 of the 3 percentage points by which GDP increased.

Federal spending increased by 2.8 percent in 2018 afer having increased by only 0.3 percent in 2017. Te higher federal outlay helped boost total government’s contribution to GDP to 0.32 points, up from 0.16 points in 2017.

Growth slowed to a more trend-like pace in 2019 when it registered 2.2 percent annually. Increased foreign trade tensions created uncertainty and dampened business fxed investment, although consumer spending held up well. Expectations had been that growth would continue through 2020 if trade relations would not worsen, all other things being equal.

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U.S. economy and labor market Chapter 1

Economic growth in terms of quarterly changes in GDP over the last three years through third quarter 2020 are represented by Figure 1-1. Te break in what had been trend growth occurred with frst quarter 2020 GDP data, which show a decline of 5.0 percent. As stated by the Bureau of Economic Analysis, the decline refected the response to the spread of COVID-19, as governments issued “stay-at-home” orders in March. Tis led to rapid changes in demand, as businesses and schools switched to remote work or canceled operations, and consumers canceled, restricted, or redirected their spending. Most of the economy was open through mid-March. Te lockdown of the economy, which occurred in most states, did not really start to take efect until mid-March/early April. First quarter 2020 provided a preview of what was to come when the second quarter arrived.

Figure 1-1. U.S. gross domestic product (chained 2012 dollars), quarterly percent change, seasonally adjusted annualized rate United States, third quarter 2018 through third quarter 2020 Source: U.S. Bureau of Economic Analysis, Domestic Product and Income

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The previous pattern rate of economic growth was disrupted by the viral pandemic and efforts to contain it.

Te decline in frst quarter 2020 GDP is primarily attributable to a collapse in personal consumption expenditures (PCE), which fell by 6.9 percent, accounting for 4.75 out of the 5.0 GDP percentage points lost (Figure 1-2). Te decline in consumption made it the steepest rate of decline in forty years to that point. Te decrease in PCE mostly refected less spending on services, led by healthcare as well as food services and accommodations. Business fxed investment (BFI) spending fell by 1.3 percent and subtracted



just over one-quarter point from headline growth. Much of this was due to reduced spending in nonresidential fxed investment, primarily on transportation equipment. Te decrease in private inventory investment was mainly in manufacturing, led by petroleum and coal products.

GDP plummeted 31.4 percent in second quarter 2020. Te big drop in GDP in the second quarter largely refects the collapse in economic activity that occurred in late March through mid-May. Once again, the weakness in expenditures was concentrated on the consumer side. PCE plunged by nearly 41 percent. Te decrease in consumer spending occurred on both the goods side of the economy, led by clothing and footwear, and on the services side, led by healthcare. Spending on fxed investment by business primarily refected a decrease in spending on equipment, led by transportation equipment. Te decrease in private inventory investment primarily occurred in retail led by motor vehicle dealers. Te only real area of growth was the rise in federal government spending related to transfer payments made to individuals and to banks for processing and administering the Paycheck Protection Program (PPP) loans to businesses that were extended during the quarter.

I I I I I I I I I ----------Figure 1-2. Contributions to percent change in real GDP, seasonally adjusted annualized rate United States, third quarter 2018 through third quarter 2020 Source: U.S. Bureau of Economic Analysis, Domestic Product and Income

Contributions 2018 Q3 2018 Q4 2019 Q1 2019 Q2 2019 Q3 2019 Q4 2020 Q1 2020 Q2 2020 Q3 GDP percent change annual rate 2.1 1.3 2.9 1.5 2.6 2.4 -5.0 -31.4 33.1 Percentage contribution by factor Consumption expenditures 1.79 1.05 1.25 2.47 1.83 1.07 -4.75 -24.01 25.27 Fixed investment 0.14 0.46 0.50 -0.07 0.42 0.17 -0.27 -5.27 4.96 Change in private inventories 1.58 0.23 0.21 -0.97 -0.09 -0.82 -1.34 -3.50 6.62 Net exports of goods and services -1.83 -0.27 0.55 -0.79 0.04 1.52 1.13 0.62 -3.09 Government expenditures 0.44 -0.16 0.43 0.86 0.37 0.42 0.22 0.77 -0.68

Changes to outlays in consumer expenditures have had the most impact on the economy during the 2020 pandemic event.

Te U.S. economy bounced back to grow at an annualized rate of 33.1 percent in third quarter 2020 based on the preliminary reading of GDP. Te increase in third quarter GDP refected continued eforts to reopen businesses and resume activities that were postponed or restricted due to COVID-19. Although the third quarter growth was greater than the second quarter decline, the level of GDP is still roughly 3.5 percent below the peak level hit late last year before the pandemic hit. In other words, the size of the American economy at present is 3.5 percent smaller than it was immediately before the COVID-19 pandemic struck the economy.



Much of the turnaround in third quarter GDP growth was supported by the same factors that were involved with the second quarter GDP decline. Consumer spending surged by 40.7 percent annualized to drive the resurgence. Spending was aided by a jump in incomes provided by stimulus checks and expanded unemployment benefts earlier in the summer. Te increase in PCE refected increases in spending on services (led by healthcare as well as food services and accommodations) and goods such as clothing and footwear. Consumers shifing spending away from close-contact services (such as mass transit) towards durable goods (such as on cars) also explains the upturn.

Business spending on equipment jumped 70.1 percent in third quarter, easily ofsetting the 35.9 percent drop that occurred in the second quarter. Spending on transportation equipment led the increase. Residential construction leaped up 59.3 percent. Record low mortgage rates and shifing preferences for more livable space have led to a recovery in both single family construction and home sales. Spending on nonresidential structures was of, however, falling 14.6 percent during third quarter. Tis marks the fourth consecutive quarter in which this spending component has declined. Low energy prices continue to curtail oil and gas drilling activity, and rising vacancy rates are a huge obstacle for new commercial construction. A sharp increase in private inventory investment led by retail motor vehicle dealers boosted GDP growth in the third quarter by 6.6 percentage points. Decreases in federal government spending refected a decline in transfer payments and fewer fees paid to administer the PPP loans. State and local government spending also declined.

Consumer spending reacts to pandemic policy moves Te economy tends to rely upon the spending propensity of its consumers. Consumer spending makes up the greatest dollar-wise contribution to GDP, accounting for roughly 70 percent of total output value annually. Real personal consumption expenditures (PCE) which account for household spending, depend on both the willingness and ability of the consumer to spend as well as the number of potential consumers earning income to fund the consumption. To that end, the ability to spend largely is a function of income earned afer taxes, or disposable personal income (DPI), as well as the opportunity to earn income, which mostly comes from wages and salaries earned from employment. Transfer payments to individuals, primarily from government in the form of social benefts, also contribute to DPI. Social beneft payments are those that are provided through public programs such as Social Security, Medicare and Medicaid, and unemployment insurance benefts. Over the years 2017 through 2019, wages and salaries made up roughly 57 percent of DPI on average while government transfer payments made up about 19 percent.



Te states’ response to contain the pandemic resulted in the closure or capacity restrictions of many businesses. Stay-at-home guidelines added to the unprecedented number of individuals who became unemployed. To provide economic relief to individuals and fscal stimulus to the economy, the Coronavirus Aid, Relief, and Economic Security (CARES) Act was passed by Congress and signed into law by President Trump on March 27, 2020. In addition to providing aid to afected businesses, a variety of measures were designed to help household members. Te CARES Act expanded unemployment insurance benefts provided through three programs. Te Federal Pandemic Unemployment Compensation (FPUC) program provided a temporary weekly supplemental payment of $600 for people receiving unemployment benefts to the end of July 2020. Te Pandemic Unemployment Assistance (PUA) program provided unemployment benefts to people who are not usually eligible for state unemployment insurance benefts to the end of December 2020. Te Pandemic Emergency Unemployment Compensation (PEUC) program provided unemployment benefts for an extra 13 weeks to people who exhausted all available weeks of state unemployment benefts (26 weeks in Washington) to the end of December 2020. Te CARES Act of 2020 also provided $300 billion in direct support economic impact payments to individuals, with advance tax rebate payments distributed mostly in April 2020. A $1,200 refundable tax credit payment was provided to individuals ($2,400 for joint taxpayers) that met specifed criteria. In addition, qualifed taxpayers with children received $500 for each child.

Te extensive shutdown in economic activity put in place to contain the COVID-19 outbreak resulted in a massive surge in unemployment claims and an abrupt decline in nonfarm payroll employment. Te downturn carried into April and began to bottom out near the end of May as businesses began to gradually reopen. Te loss in employment produced a decline in wages and salaries in second quarter 2020 (Figure 1-3). Government transfer payments to people increased in the second quarter dramatically however, more than making up for the lost wage and salary income for many. As more people began returning to work in the third quarter and as the one-time direct economic impact payments ended, wage and salary income growth resumed while the level of transfer payments fell.



Figure 1-3. Percent change in wage and salary income and government transfer receipts, seasonally adjusted annualized rate United States, 2018 through third quarter 2020 Source: U.S. Bureau of Economic Analysis, Domestic Product and Income

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Wage and salaries dipped in second quarter 2020 before rebounding in third quarter. Meanwhile, government transfer payments surged in second quarter.

Real disposable personal income (DPI), a measure of income afer accounting for taxes and adjusting for infation, has been rising with the economic expansion. Its rate of increase will not necessarily be positive from one period to the next, since the level of income generated will change as economic growth changes. A measure of consumer spending is personal consumption expenditures (PCE). All things being equal, it is expected that PCE should move in direct relation to DPI. In practice however, changes in consumers’ willingness to spend will occur as other factors change. Tis could motivate consumers to change the proportion of their incomes they spend, along with their savings rate. Consumers might also consume more than their income in any one-time period, either by borrowing from their savings or from fnancial institutions, depending on interest rates and credit availability.

PCE continued to rise during most of frst quarter 2020 since the reaction to the virus occurred chiefy in March (Figure 1-4). As April unfolded, PCE plummeted even as DPI rose due to the receipt of government transfer payments. Te difcult March through April period was enough to outweigh a resurgence in spending that began in late May and carried into the third quarter. Extreme circumstances resulted in consumers been asked to stay in their homes and avoid social interaction in the early months of the pandemic. Among many other things, this behavioral



shif has meant an outright inability for consumers to spend on various services from low cost experiences like a haircut or a trip to the movies to more signifcant outlays like air travel and medical procedures. Although these represent a few examples, they are indicative of the large areas of consumption opportunities that closed.

Figure 1-4. Personal change in real personal consumption expenditures and disposable personal income, seasonally adjusted annual rate United States, 2018 through third quarter 2020 Source: U.S. Bureau of Economic Analysis, Personal Income and Outlays

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Consumption activity declined and then plummeted during the first half of 2020 but rebounded in third quarter 2020.

Te CARES Act passed earlier in the year ofered a bridge to get afected households to the other side of the crisis and a way to stimulate the economy at the same time. Consumer spending rebounded in the third quarter, with most of it directed toward consumer goods. Afer years in which the share of spending at bars and restaurants rose steadily, consumers rediscovered the grocery store, which helped to lif spending on non-durable goods above its pre-recession peak. Spending on durable goods also surged.

Te fact that goods spending rebounded so swifly suggests the CARES Act had the desired simulative impact.

Te inability of consumers to pursue service-spending opportunities resulted in some sense a “forced savings” efect. At the same time, government transfers to people were surging. Te combination of regular, state-level unemployment benefts, plus the extra $600 per week from the federal government, led to average wage replacement levels that exceeded 100 percent in some states.



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Tis unspent money on services coupled with the rise in income from transfers caused personal savings to rise to a record level in April. Tus, personal savings as a share of personal disposable income, or what is known as the personal savings rate, shot up to 33.6 percent in April (Figure 1-5).

To put things in perspective, the personal saving rate had averaged 7.5 percent in the year leading up to COVID-19. Personal spending on services rebounded to a respectable level in third quarter, but did not completely reverse the collapse in spending on services that occurred in the second quarter. As such, the savings rate remains at an elevated level.

Figure 1-5. Personal savings as a percentage of disposable income, seasonally adjusted annual rate United States, September 2016 through September 2020 Source: U.S. Bureau of Economic Analysis, Personal Income and its Disposition

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Consumers savings shot up in April 2020 but were used to bolster consumption later.

Retail sales data provide another measurable way to track consumer spending along with the manner to which consumers allocate expenditures towards the purchase of durable and nondurable items ofered by retailers. Tey also provide an indication of the demand for certain retail products and how that might afect hiring decisions within those industries. Since the data are available monthly, it can provide an early indication of how consumer spending is progressing. Retail sales are reported in nominal dollars, and sales value can be volatile since they are afected by price movements of items typically purchased like gasoline. Focusing on longer-term trends helps to navigate through some of this volatility. Annual retail sales grew by 3.5 percent from 2018 to 2019, somewhat less than the 4.4 percent pace established during the same



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period a year ago. January 2020 started the year of with sales up 0.8 percent for the month before buying behavior began to radically change starting in February (Figure 1-6). Te bottom fell out for retailers as the lockdowns took efect in March and April, but sales rebounded strongly in May and June. From there through September, sales continued to grow to where 2020 receipts from January through September are now less than 1.0 percent of what they were during the same period in 2019. Renewed restrictions placed on establishments during November and into December, however, look to end the upward climb.

Figure 1-6. U.S. retail sales, month over month and year over year, seasonally adjusted percent change United States, September 2018 through September 2020 Source: U.S. Census Bureau, Monthly and Annual Retail Trade Report

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Increases in retail sales were choppy moving into 2019, but stabilized until this past September.

Based on retail data from February through September, it is possible to see how sales have fared throughout the pandemic up to that point. Te sales fgures for retailers in 2020 can be contrasted with 2019 to see how sales have changed. Since there has been a recent spike in case counts, it probably means that consumer demand is set to shif again. Having an understanding of how things played out in the retail sector during the initial lockdown can inform the outlook for consumer spending amid the resurgence of COVID-19 cases.

While the overall level of sales is nearly back to where it was, outcomes vary dramatically by store type (Figure 1-7). In this case, sales from February through September 2020 are compared with sales from the same period one year prior. Tose diferences that have arisen could be the deciding factor between either making it to the other side of this crisis or literally closing shop for many stores.



Figure 1-7. Percent change in nominal retail sales by retailer, seasonally adjusted United States, February through September 2020 compared to February through September 2019 Source: U.S. Census Bureau, Monthly Retail Trade Report

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Nonstore retailers Building material and garden supply stores

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Even with the third quarter rebound, sales at most retail establishments in 2020 lag 2019.

While total retail sales for February through September in 2020 are nearly at the level they reached in 2019, most retail stores have not matched their 2019 receipts. Clothing stores sufered the largest sales declines in March and April. Sales came roaring back in August and September, but still have a long way to go to reach 2019 levels. Restaurants, which had been limited to take-out in certain cases and capacity restrictions, were able to extend dine-in services within the frst or second phase of many state re-opening plans. Tis likely led to a pick-up in restaurant visits, as the opportunity to eat out again rather than cook at home was presented. Restaurant sales surged beyond their April low, but remain down from January and February monthly levels. Te accelerating COVID-19 case counts, and corresponding containment measures puts the restaurant recovery at risk, however.

Sales at gasoline stations fell during March and April before rebounding. Te rebound at gas stations was partly price related as demand picked up amid what are still low levels of mobility. Electronics and appliance stores and furniture and home furnishing stores also re-opened and experienced rising sales. Both categories experienced signifcant sales increases in June, as did miscellaneous stores, which include forists, used merchandise, and pet supply shops.

Motor vehicle and parts dealers have seen monthly sales volume rise above January and February levels in recent months. Te 2020 sales receipts are now roughly in line with those recorded in 2019. Autos sales will likely



continue to support total retail sales in the months ahead if dealerships are not required to close. Given the fast comeback in auto sales, the decline in sales was probably associated with dealership closures.

Nonstore retailers, or online sales, is the one category of retail that continued to see monthly sales increase through May. Online sales got a lif as many people turned to virtual options while confned to their homes. Te shif to online was in many ways a continuation of a pre-existing trend, as online sales have been outpacing more traditional forms of retail for many years. Online sales should continue to post gains, particularly since many re-opening plans are being delayed.

Grocery stores were the primary benefciary of the stay-at-home orders that were enacted in March. Panic buying gripped the nation during the initial onset of the pandemic in early March, and shoppers piled carts high with everything they thought they might need for a long stretch at home. Sales surged in March as people stocked up on basic food items, toilet paper and cleaning supplies. Sales volume also increased in March at health and personal care stores, (which ofen carry, among other things, toilet paper) and general merchandise stores (which include warehouse clubs such as Costco). Grocery sales then declined in April afer the March round of panic buying and have since leveled of, albeit at monthly levels above January and February. Monthly sales at health and personal care stores, along with general merchandise stores, declined in March before rising to or above March levels most recently.

Building material and garden equipment stores held up well during the crisis, with February through September sales in 2020 exceeding 2019 by 13 percent. Part of the success in this category is attributable to retailers such as Home Depot, that were determined to be essential businesses during the lockdowns. Building material sales should continue to hold up due to the improvement taking place in home sales.

Business fixed investment spending holds up reasonably well Business fxed investment (BFI) entails spending by businesses on residential and nonresidential structures, equipment and intellectual property products, the major type being sofware. Tis type of investment is expressed as “fxed” to distinguish it from investment in inventories. Spending on equipment, which is a component of nonresidential investment, constitutes the largest dollar outlay for businesses.



Real investment spending by American businesses totaled $3.4 trillion in 2019, which was a little more than 17 percent of overall GDP. Trade uncertainty, a global slowdown and a strong U.S. dollar had contributed to a slowing in business investment in fourth quarter that year, as it grew by only 1.0 percent during that time (Figure 1-8). In addition to the overall economic climate, Boeing’s best-selling 737 MAX aircraf had been grounded. Most of the fallof had occurred in equipment spending, including transportation equipment. Investment in equipment subsequently peaked in frst quarter 2019 before declining during the next three quarters as businesses grew increasingly cautious.

Te arrival of the pandemic had the same efect on business investment plans as it did in almost every other economic measure – the bottom fell out in March and April. Yet despite the major collapse in GDP over the frst two quarters of 2020, business investment declined even less than it did during the Great Recession. Te relatively modest pull back in investment since the economic fallout has been mostly tied to the service sector, which tends to be less capital intensive. In addition, companies needed to facilitate working from home arrangements and reinvent themselves during the pandemic, resulting in a shif in investment needs. Equipment categories like computers and information-related products saw increased demand, thus cushioning the blow to investment from the virus. Te demand for medical equipment also rose.

Figure 1-8. Real business fixed investment, quarterly percent change, seasonally adjusted annual rate United States, third quarter 2018 through third quarter 2020 Source: U.S. Bureau of Economic Analysis, Domestic Product and Income

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Business fixed investment was disrupted by the virus, but its decline was moderated.



BFI rebounded sharply in third quarter 2020, but has yet to return to pre-pandemic levels. Investment plans have been growing, and there could be some action taken on delayed investment since equipment spending peaked a full year before the pandemic. However, as the virus lingers, overall demand looks set to remain weak, which will limit the incentive companies may have to invest in the near term.

Construction spending moderates, then bounces back An important category of private fxed investment is the construction of new residential and nonresidential structures. Te data provide detail into various sectors of the economy and can indicate where the economy is headed. Although the initial construction spending data are subject to large revisions, trends over several months refect hard data. Decisions to build a home, ofce building, warehouse, power plant or manufacturing facility involve some deep thinking and the implications from those decisions are long term.

Total private construction spending outlays had been generally increasing as the most recent economic expansion strengthened. Spending rose by 2.3 percent overall in calendar year 2019 (Figure 1-9). Federal Reserve policy had moved to a more accommodating stance when it cut interest rates three times in 2019. Te moves created a more favorable climate for construction activity, and construction spending consequently rose by 1.9 percent over the month in January 2020.

Figure 1-9. Value of total construction put in place, billions of dollars, seasonally adjusted annual rate United States, September 2017 through September 2020 Source: U.S. Census Bureau, Construction Spending

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It did not take long for the efects of the coronavirus to be felt. Construction spending declined in March, April and May. Spending started rebounding afer that, but the rebound has been concentrated on the residential side. Nonresidential spending has instead been languishing (Figure 1-10).

Figure 1-10. Value of residential and nonresidential construction, millions of dollars, seasonally adjusted annualized rate United States, September 2017 through September 2020 Source: U.S. Census Bureau, Construction Spending

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Residential construction activity has supported the rebound in construction spending in 2020 while nonresidential construction has weakened.

Spending on nonresidential construction is more pronounced and occurs at a level roughly 40 percent higher than residential construction. Normally, lower interest rates tend to reduce commercial mortgage rates and boost demand for income-producing properties. However, the efects of the COVID-19 crisis have heightened uncertainty regarding future tenant cash fows and creditworthiness. Demand has turned sluggish and continues to weigh heavily on most commercial and institutional construction. Nonresidential spending declined 1.6 percent during the month of September 2020, the fourth consecutive monthly drop.

Measures taken to contain the coronavirus have created major difculties for commercial real estate. Hotel and retail properties have faced the most immediate short-term pain for many businesses. With a steep drop-of in travel of all forms, hotel occupancy has declined sharply. Hotel occupancy rates have improved since the spring but remain severely depressed. Occupancy has only moved sideways since mid-June when the coronavirus reappeared in many parts of the country.



Avoidance of public spaces has also weighed heavily on retail properties, especially those deriving foot trafc from nearby gyms, restaurants, breweries, movie theaters and other entertainment venues. Overall, the uncertainty surrounding the coronavirus has curtailed new retail development, as many projects are delayed or sidelined until the virus subsides. Ofce spending is another area that remains depressed. Te number of ofce tenants has declined with many workers working from home and with the long-term needs of tenants being uncertain.

Residential construction has been supported by the housing industry, which has been one of the economy’s few bright spots. Te housing market had strong momentum to start the year. A healthy job market and low mortgage rates made homes more afordable for many consumers, although values crept steadily higher throughout 2019. Sales of new homes totaled 774,000 in January 2020, the highest monthly fgure since October 2007 (Figure 1-11). Although sales slowed slightly in February, they were running 16 percent of the same pace the year before.

Figure 1-11. Conventional 30-year mortgage rates and new home sales, thousands of units, seasonally adjusted annualized rate United States, September 2017 through September 2020 Source: Federal Housing Finance Agency; U.S. Bureau of Economic Analysis, New Residential Sales

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Sales of new homes in 2018 started slumping as mortgage rates begin to rise.

Te sudden stop in economic activity that began in mid-March slowed home sales growth abruptly, but not to a major degree. Although home showings were banned in many areas during April, new homes are much more conducive to virtual showings, which makes social distancing less



of an issue. Te support this provided to sales also allowed residential construction activity to keep from cratering and allowed builders to add to inventories. Home construction was deemed an essential activity in most communities that enacted shelter-in-place orders, and most work can be done in ways that are consistent with social distancing guidelines.

New home sales bottomed out in April and have since come roaring back. Record low mortgage rates have spurred a rebound in the housing market and encouraged more higher income renters to become homeowners. On top of this, there has been a shif in housing preferences away from dense urban areas to larger spaces in the suburbs that can more easily accommodate home ofces and gyms. With people spending more time at home during the pandemic, many households are looking for more space to accommodate remote workspaces, as well as the presence of more people in the home for more of the day. New homes provide homebuyers better options, as new homes are more apt to be internet friendly and have more open space. Te shif to homeownership has also increased spending for additions and renovations.

Te resulting divergence between strengthening residential activity and weakening commercial construction became clearer in August. Overall construction spending rose 2.0 percent during the month, an outcome fueled entirely by a 6.8 percent jump in residential spending. Tis was the third consecutive monthly gain in residential outlays, and residential construction spending has risen at a 3.5 percent annual rate from June through September.

Government enacts fiscal policy measures to combat economic fallout With the United States economy facing an unprecedented shutdown of economic activity in March 2020, the federal government moved quickly to pass a series of fscal policy bills designed to keep the economy afoat. Four major laws were enacted in phases. Te frst, the Coronavirus Preparedness and Response Supplemental Appropriations Act, was passed on March 6, which spent approximately $8 billion on support for things like the development of vaccines and therapies. Te second law, the Families First Coronavirus Response Act, passed on March 18, was more expansive and consisted of a mix of public health and economic support measures. Some of the larger items included an increase in the federal government’s matching rate for Medicaid ($50 billion), employer tax credits for paid sick leave and paid family medical leave ($95 billion) and more generous supplemental nutritional assistance program (SNAP) benefts ($21 billion).



Te CARES Act, enacted on March 27,was easily the biggest fscal package in terms of size. Major provisions included direct payments to households ($293 billion), the creation of the Payroll Protection Program (PPP) for small businesses ($377 billion), a dramatic expansion of unemployment insurance benefts ($268 billion) and numerous other policy changes designed to support businesses, households, and those responding to the public health crisis.

Te fourth and fnal bill was enacted on April 24 and included additional money for the PPP ($321 billion) and more money for healthcare providers and COVID-19 testing eforts ($100 billion). Altogether, the measures adopted across the four laws represented an enormous expansion of fscal support that unfolded in just a six-week period. Te increase in spending most efectively took place over the second quarter of the year (Figure 1-12).

State and local government expenditures declined in the second quarter. Unlike the federal government, most state and local governments must balance their budgets. Te severe economic downturn caused a sharp contraction in income and sales tax receipts for state and local governments. Absent federal help, these governments are ofen forced to cut employment and capital expenditures going forward, which helped contribute to another decline in third quarter 2020. State and local governments have gotten some fscal aid in previous federal packages, including expanded federal cost-sharing for Medicaid and access to a $150 billion Coronavirus Relief Fund that covers expenses made specifcally in response to the pandemic. But while these and some other measures have been passed, there has not yet been a large appropriation of federal funds to cover general revenue shortfalls.



Figure 1-12. Government purchases and gross investment, trillions of dollars adjusted for inflation, seasonally adjusted annualized rate United States, third quarter 2018 through third quarter 2020 Source: U.S. Bureau of Economic Analysis, Government Current Receipts and Expenditures

Expenditures 2018 Q3 2018 Q4 2019 Q1 2019 Q2 2019 Q3 2019 Q4 2020 Q1 2020 Q2 2020 Q3 Government expenditures percent change annual rate 2.5 -0.9 2.5 5.0 2.1 2.4 1.3 2.5 -4.9

Percentage change from preceding period Federal government expenditures 4.5 1.9 1.3 9.2 4.8 4.0 1.6 16.4 -6.2 National defense 5.4 6.4 5.6 4.4 5.6 5.2 -0.3 3.8 2.2 Nondefense 3.3 -4.4 -4.7 16.9 2.8 0.1 4.4 37.6 5.2 State and local government expenditures 1.4 -2.5 3.2 2.6 1.6 1.5 1.1 -5.4 -4.0

Federal government spending escalated in second quarter 2020 to support economic stimulus and relief policy measures.

In August, President Trump issued a series of executive orders. Te orders asked the U.S. Treasury to allow for a deferral of payroll taxes from September 1 through December 31 for workers who earn approximately $100,000 or less per year. Te payroll tax holiday is not mandatory, so it’s possible many employers did not participate. Second, the orders established a “Lost Wages Assistance” program using Federal Emergency Management Agency emergency grant funding to provide $300 (with an optional state match of another $100) for states to supplement weekly unemployment beneft payments following the expiration of the $600 weekly FPUC payment at the end of July. Ultimately, the FEMA fund had enough for states to provide the enhanced beneft for up to 6 weeks. Finally, the orders ensured interest on student loans held by the federal government were waived through the end of 2020, and payments could be deferred until December 31.

Labor market receives devastating blow from COVID-19 Two surveys are used by the U.S. Bureau of Labor Statistics (BLS) to measure national labor market trends. Te establishment survey provides an estimate of the number of occupied jobs in the private and public sectors (federal, state and local government). Te survey of households samples roughly 60,000 out of about 125 million households in the country and estimates the number of people either employed or unemployed but searching for a job.1

Te U.S. labor market had been enjoying the benefts of a record-long expansion through February 2020. Hiring had been steady, and job market conditions were seen as tight, with the economy hovering around what economists consider full employment.

1 The estimate of the number of households in the United States comes from the quarterly Homeownership and Vacancy report published by the U.S. Census Bureau.



Employment in the United States nosedived in March, however, as shelter-in-place restrictions ordered by most state governors to combat the spread of COVID-19 kicked in. Total nonfarm payroll employment tumbled by 1,373,000 (Figure 1-13). Job losses mounted in April, making the losses in March seem almost like a footnote. Nonfarm payrolls plunged almost 20.8 million with job losses spreading across all industry sectors. Many who lost their job were unable or unwilling to look for a new one, either because their industry had been largely shutdown, they did not feel safe returning to the workplace, they were taking care of children who were out of school/daycare, or they were disincentivized to look for work while receiving augmented unemployment insurance benefts under the CARES Act.

Hiring started to pick up in May as states began to loosen restrictions. Employment rebounded by more than 2.7 million and began a bit of a turnaround in the labor market. June followed with an even sharper increase in employment. Employers added jobs for the ffh consecutive month in September, bringing the total number of jobs recovered from the virus-related low to 11.5 million. Tis represents just over half of the jobs that were lost in March and April.

Figure 1-13. Total monthly nonfarm employment in thousands, seasonally adjusted United States, February 2020 through September 2020 Source: U.S. Bureau of Labor Statistics, Current Employment Statistics

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Although the labor market has continued to improve, the pace of job growth is moderating. Te latest spike in COVID-19 infections is negatively afecting restaurants, bars, entertainment venues and in-person shopping. Restaurant closings have also picked up. Te fallout is likely to become more apparent in the December and January reports.

Job losses have been widespread across industries since February 2020 (Figure 1-14). Te leisure and hospitality industry, which includes restaurants and bars, hotels, and entertainment and recreation, lost almost half of its jobs during the shutdown period. As the economy was reopening, it gained back a good many of lost jobs, but still ranks as the industry sufering the most losses.

Education and health services, and professional and business services rank two and three respectively with respect to jobs lost. Education in this industry refers to public and not private education, and the survey measures employment associated with public school systems. Tere has been a shif to remote learning since the pandemic, and although many teachers will still be paid, the support staf for school facilities are directly afected. Te demand for health services has declined as many would-be patients are delaying and putting of many medical visits.

Professional and business services include many jobs associated with ofce work and employment services. Since many of these are in ofce buildings or malls with large visitor trafc, employment has been restricted. In professional and scientifc services, many can work remotely allowing this category to hold up better.

Figure 1-14. Nonfarm employment loss by industry in thousands, seasonally adjusted United States, February 2020 through September 2020 Source: U.S. Bureau of Labor Statistics, Current Employment Statistics

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Nonfarm employment has expanded in all major industries but one in the past year.



Unemployment rate shocked out of recent downward path Te headline unemployment rate is based on the national household survey and is arguably the most widely used single indicator of labor market conditions. It had been descending during the economic expansion leading up to the pandemic and was at a 50-year low of 3.5 percent in February 2020 (Figure 1-15).

Te huge drop in payroll employment in March and April elevated the unemployment rate, frst to 4.4 percent in March and then to 14.7 percent in April. In order to maintain consistency, the BLS household survey measures the number of employees that were on business and government payrolls during the pay period that contained the 12th of the month. Most of the increase in initial claims for unemployment insurance benefts, which factor into the unemployment rate, occurred in the two weeks following the survey week. Tis helps to explain the diferences in magnitude of the rise in the March unemployment rate versus the decline in March payroll employment.

Unemployment rates began declining as businesses gradually reopened and certain restrictions were lifed. Te unemployment rate descended to 7.9 percent in September and was on track to fall further before COVID-19 cases began rising later in the year. A new round of restrictions has been reinstituted by a number of states, which look to change the course of the unemployment rate again in December and January.

Figure 1-15. Monthly unemployment rate, seasonally adjusted United States, September 2016 through September 2020 Source: U.S. Bureau of Labor Statistics, Local Area Unemployment Statistics

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The national unemployment rate spiked when the economy halted but improved when businesses gradually reopened.



Monetary policy Between December 2015 and December 2018, the Federal Open Market Committee (FOMC) of the Federal Reserve Board (Fed) had elected to raise its target range for the federal funds rate 225 basis points (bps), or 2.25 percent. Increasing trade uncertainty and deteriorating growth abroad, however, ultimately pushed the FOMC to rapidly reverse itself in 2019 (Figure 1-16). By signaling a shif in policy and cutting rates by 75 bps over three meetings, the FOMC helped assuage fears of recession and revitalize the nation’s struggling housing sector.

Growing concerns over the economic impact of the virus outbreak prompted the FOMC to cut rates 50 bps on March 3, two weeks ahead of their scheduled meeting on March 18. Fed Chairman Powell had suggested on February 28 that the committee was prepared to ease policy by stating that the Federal Reserve would use its “tools and act as appropriate to support the economy.” On March 15, the FOMC acted again. Tis time it moved to return the target range to 0.00 percent to 0.25 percent and restart its quantitative easing (QE) program. Tis program consists of purchases of fnancial assets, including U.S. Treasury securities and mortgage-backed securities (MBS) to support the credit markets.

Since that time, the FOMC has made no signifcant changes to its policy position. Furthermore, the committee has indicated that it expects to remain in an accommodative policy state for quite some time while acknowledging the growth path for the economy is largely dependent on “the evolution of the virus outbreak and the eforts undertaken to contain it.”



Figure 1-16. Selected interest rates United States, September 2017 through September 2020 Source: Federal Reserve Board, Federal Housing Finance Agency

, ,

Inte

rest

rate

5.0%

4.5%

4.0%

3.5%

3.0%

2.5%

2.0%

1.5%

1.0%

0.5%

0.0%

10-year bond 3-month T-bill 30-year fixed-rate mortgage

Sep-17 Sep-18 Sep-19 Sep-20

Federal Reserve Board policy measures in March 2020 hastened the path for falling interest rates.



Chapter 2: Washington’s economy and labor market Washington’s level of economic growth, as measured by state GDP, has grown at an annual rate exceeding that of the nation consistently since 2011, while income and employment growth ranked it among the top leading states. Te stronger economic growth had propelled the unemployment rate down to a historic series low of 3.8 percent in February 2020. Te favorable economic climate had been instrumental in luring waves of job seekers, thereby driving up the demand for homes and commercial space.

Te COVID-19 outbreak in early 2020 altered the state’s economic good fortune and put its resiliency to the test. Washington was one of the frst states to sufer an outbreak and one of the frst to put in place aggressive mitigation eforts. Tose eforts caused the level of economic activity in the state to sharply pullback. Massive job losses resulted in March through May and the state’s unemployment rate spiked upward.

Relief measures to assist the state were undertaken by the federal and the state government to limit the damage, and there has been some turnaround within the state economy since May. Still, the path forward for the Washington state economy rests upon the prevalence of the pandemic, and society’s ability to control and eradicate the virus.

Washington state’s strong GDP growth ends in 2020 Washington’s level of economic activity can be measured by the value of the goods and services it produces at some point in time. Tis measure of the economic output of the state, formerly known as gross state product and now known as state gross domestic product (GDP), is the sum of all value added by industries within the state. It is the counterpart to the nation’s GDP.

Te U.S. Bureau of Economic Analysis (BEA) computes state GDP annually and quarterly. Changes in state GDP can be used as a measure of state economic growth, much as changes in national GDP are used to measure national economic growth.

Washington state’s real GDP growth has been outpacing that of the nation for most of the expansion. Washington ranked frst based on annual growth among all U.S. states and territories in 2017 and 2018. Its GDP expanded by 4.6 percent in 2019 (Figure 2-1), ranking it second among all states and outpacing the 2.2 percent growth achieved by the nation.


Chapter 2 Washington’s economy and labor market

Te continued development of the state’s tech sector has been largely responsible for catapulting the economy into the upper tier. Technology is largely manifested within the information services industry and in professional and business services. Information services, which includes sofware development, contributed 1.9 percentage points out of the 4.6 percentage points by which Washington’s economy expanded in 2019. Te second-largest contributor was professional and business services. Tis industry accounted for 0.53 percentage points of the total growth in real GDP, led by its professional, scientifc and technical services subsector, which contributed 0.33 of the total 0.55 percentage points.

Based on current dollar value, Washington’s GDP of $613,997 million in 2019 made it the nation’s 10th largest state economy. Te largest industry in Washington was fnancial activities. Tis industry accounted for 17.8 percent of Washington’s GDP and had annual real growth of 4.2 percent. Te second-largest industry was information, which accounted for 14.0 percent of Washington GDP and had an annual real growth rate of 15.0 percent.

Te state economy, much like the national economy, saw its level of economic activity decline shortly afer the outbreak in the frst quarter 2020. Washington state’s GDP declined by 2.6 percent, and then by just over 25 percent as the shutdown carried over to the second quarter (Figure 2-1). Washington was not alone. Real gross domestic product (GDP) decreased in all 50 states and the District of Columbia in the frst and second quarters of 2020, according to statistics released by the U.S. Bureau of Economic Analysis. Te percent change in real GDP in the frst quarter ranged from -1.3 percent in Nebraska to -8.2 percent in New York and Nevada, and from -20.4 percent in the District of Columbia to -42.2 percent in Hawaii and Nevada.

State statistics at this point have not yet been made available for third quarter 2020. Te nation’s GDP rebounded to show a growth rate of 33.1 percent in third quarter, and Washington should fare comparably. Te tech industries that have contributed most to GDP have beneftted the most from a shif to remote work, which has ofset some of the fallout from business closures. Tis should continue into the third quarter.

Washington’s economy and labor market Chapter 2

Figure 2-1. Gross domestic product, (chained 2012 dollars), annual and quarterly percent changes, seasonally adjusted annualized rate United States and Washington state, 2017 through second quarter 2020 Source: U.S. Bureau of Economic Analysis, Domestic Product and Income

■ ■ -35%

-30%

-25%

-20%

-15%

-10%

-5%

0%

5%

10%

U.S. Washington

Perc

ent c

hang

e in

GDP

2017 2018 2019 2020 Q1 2020 Q2

Washington’s economy declined along with the national economy in the first half of 2020.

Personal income impacted by business shutdowns and government transfer payments Te growth of Washington’s GDP during the expansion had occurred amidst higher employment and income for the state’s residents. Figure 2-2 shows how personal income growth in Washington compares with the U.S. Te pattern of income growth is closely related to GDP growth making the results in Figure 2-2 look very similar to those in Figure 2-1 up until 2020. In 2019, Washington had a personal income of $493,128 million ranking it 13th in the U.S. From second quarter 2018 to second quarter 2019, the level of personal income in Washington grew by 6.5 percent, while U.S. personal income grew by 4.9 percent.

Personal income includes net earnings by place of residence; dividends, interest, and rent; and personal current transfer receipts received by the residents of Washington. Wages and salaries make up the largest component of personal income. Te annual growth in wages and salaries for Washington state is shown in Figure 2-3. Te wages are expressed in nominal terms, unadjusted for infation. Personal income increased by over 30 percent in both Washington and the U.S. in second quarter 2020, even as wages and salaries were decreasing. Te diference was ofset by a massive increase in government transfer payments brought about from the various fscal stimulus and relief packages passed into law




by Congress. Personal transfer receipts to Washingtonians more than doubled during the second quarter, but declined in the third quarter. Wages and salaries rose again in the third quarter when businesses gradually reopened.

Figure 2-2. Personal income, (current dollars), annual and quarterly percent changes, seasonally adjusted annualized rate United States and Washington state, 2017 through third quarter 2020 Source: U.S. Bureau of Economic Analysis, Personal Income and Outlays

■ ■

-20%

-10%

0%

10%

20%

30%

40%

U.S. Washington Perc

entc

hang

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pers

onal

inco

me

2017 2018 2019 2020 Q1 2020 Q2 2020 Q3

Washington’s level of personal income, like the nation’s, rose during the second quarter of 2020 before declining in the third.

Figure 2-3. Percent change in annual and quarterly wages and salaries, current dollars Washington state, 2017 through third quarter 2020 Source: Employment Security Department/LMEA; U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages

40%

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e in

aver

age

wage

30%

20%

10%

0%

-10%

-20% Wages and salaries

2017 2018 2019 2020 Q1 2020 Q2 2020 Q3

Wage growth in Washington state resumed in third quarter 2020 as businesses partially reopened.


Given that the drop of in wages and salaries paid by businesses in second quarter 2020 was so dramatic, it is worthwhile to see how each major industry was afected over the course of the year. Figure 2-4 shows on a percentage basis the degree by which nominal wages and salaries changed by industry sector from second quarter 2019 through second quarter 2020. Total wage and salaries during this period increased in three out of the four quarters, leading to an overall increase of 3.9 percent. By comparing each industry to the total, it can be seen which industries were less impacted and which were impacted more.

Wages and salaries would have been paid to employees within each industry group, so the percent change in industry wages and salaries paid should be directly related to employment and job losses. Te largest impact of the pandemic has been on businesses that require a high degree of customer contact. Te restaurant industry, bars and entertainment venues have been hardest hit. Business establishments in these categories make up the bulk of jobs in the leisure and hospitality sector. Manufacturing was also heavily impacted, primarily in aerospace product and parts manufacturing. Te demand for aircraf in 2020 had been declining as airlines around the world reduced the number of fights in response to fewer people not being able to travel. Other services, which include hair and beauty salons, and auto repair and maintenance, paid out roughly the same amount they did one year prior.

Industries whose wages and salaries grew at a rate above the state average and were less impacted by the virus include information, fnancial activities, retail trade, and professional and business services. Information comprises much of the tech sector, along with a portion of professional and business services. Retail trade includes online retail stores, whose sales grew with increased online shopping under various social distancing guidelines and restrictions. All the industries with above average wage and salary growth also have the advantage of having many of its employees able to work from home using computer access.




Figure 2-4. Percent change in average annual wage by industry Washington state, second quarter 2019 through second quarter 2020 Source: Employment Security Department/LMEA, U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages

Information Financial activities

Retail trade Professional and business services

Washington state Government

Education and health services Transportation, warehousing, utilities

Construction Wholesale trade

Mining and logging Other services Manufacturing

Leisure and hospitality

-10% -5% 0% 5% 10% 15%

Indu

strie

s

On a percentage basis, wages and salaries grew most annually in information and financial activities and declined the most annually in leisure and hospitality during second quarter 2019 to second quarter 2020.

Retail sales reach record high of $181 billion in 2019 Income and employment growth leading up to 2020 generated more consumer spending resulting in increased retail sales receipts over that time. Figure 2-5 shows how taxable sales have risen annually from 2015 through 2019. In 2019, taxable retail sales increased by roughly 6.1 percent or $11.0 billion from 2018, pushing total taxable retail sales to a record high of $181.1 billion.


Figure 2-5. Annual taxable retail sales, millions of dollars Washington state, 2015 through 2019 Source: Washington State Department of Revenue

$95,000

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Taxa

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reta

il sale

s in

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2015 2016 2017 2018 2019

Retail sales had consistently grown during the expansion.

Te present available data are unable to account for the impact the pandemic has had on annual taxable retail sales, but by examining the data on a quarterly basis, it is at least possible to see how second quarter 2020 compares with second quarter 2019 sales (Figure 2-6). Tere is some seasonal variation from quarter to quarter with it being most noticeable in the frst and second quarters of the year. Afer sales dip in the frst quarter following the fourth quarter holiday season, they pick up again in the second quarter. Tus, it is reasonable to believe that second quarter 2020 sales would have increased again to a level beyond second quarter 2019 had the virus outbreak not occurred. Although purely speculative, the data are still useful to see what the shortfall in second quarter 2020 sales looks like when directly compared to second quarter 2019. In this case, taxable retail sales were $5.8 billion less in second quarter 2020 than what they were one year earlier. While sales receipts in third quarter 2020 most likely exceeded second quarter 2020 levels, and while total annual 2020 sales were expected to rise above the annual 2019 level, the annual 2020 increase probably will be less than the increase registered in 2019.




Figure 2-6. Quarterly taxable retail sales, millions of dollars Washington state, second quarter 2018 through second quarter 2020 Source: Washington State Department of Revenue

$0

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2018 Q2 2018 Q4 2019 Q2 2019 Q4 2020 Q2

The falloff in retail sales in second quarter 2020 is readily observed in the quarterly data.

Figure 2-7 shows how sales in second quarter 2020 among the major retail industries compared with sales registered in second quarter 2019. Total retail sales were already noted to have been less than what they were in second quarter 2020 relative to second quarter 2019 and were down 12.7 percent. Te industry data has much in common with what occurred at the national level, with a few minor exceptions. Along with the rest of the nation, clothing stores, restaurants, furniture stores, and motor vehicle dealers in Washington all took the brunt of the decrease in sales. Nonstore retailers, which encompasses online shopping, did very well. Sales by building material and garden supply stores and grocery stores increased much like they did across the nation. Sales at electronics and appliance stores along with miscellaneous stores seemed to fare a little better than their counterparts throughout the nation.


Figure 2-7. Percent change in nominal retail sales by industry Washington state, second quarter 2020 compared to second quarter 2019 Source: Washington State Department of Revenue

Nonstore retailers

Electronics and appliance stores Miscellaneos stores

Building material and garden supply stores Grocery stores

Health and personal care stores General merchandise stores

Gasoline stations Total Retail

Motor vehicles and parts dealers Furniture and home furnishing stores

Restaurants Clothing and clothing accessories stores

-60% -45% -30% -15% 0% 15% 30%

Indu

strie

s

Nonstore retailing sales received a boost when shoppers stayed at home more during the pandemic.

Washington housing activity resumes pace after temporary setback Years of top-level economic growth had generated employment opportunity and had attracted job seekers to Washington from all over the country. Most new inhabitants come to settle in the western part of the state lying west of the Cascade mountain range with proximity to the Seattle, WA and Portland, OR areas. Now the nation’s 13th most populous state, Washington’s population growth has pushed up the need for housing and pushed home prices in these areas sharply higher. Te desire for more available space, together with more people confned to the home because of the virus, has created more of a demand for homes located farther away from urban areas where values are better and living space is more accessible.

Te virus pandemic and economic shutdown also prompted the Fed to cut interest rates which then led to record low mortgage rates. Te result further increased the demand for homes, and despite rising home prices, sales bounced back strongly afer the initial shutdown. Builders have responded by moving to build more homes (Figure 2-8) and have maintained a higher pace since June 2020.




Te housing industry, like other industries, will continue to be afected by changes in the economy that react to the evolution of the virus pandemic. Should the ability society develops to control the spread of the virus improve, and the economy recovers further, housing demand and home building activity should increase. Mortgage rates should continue to remain low for some time, as the Fed has indicated its monetary policy position will remain on hold until it is confdent a full recovery has taken place.

Figure 2-8. Housing price index and single-family housing starts, three-month moving average, seasonally adjusted Washington state, September 2015 through September 2020 Source: Federal Home Loan Mortgage Corporation, U.S. Census Bureau

280.0 6,500

240.0 5,000

Housing price index Housing starts

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ing p

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ndex

200.0 3,500

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80.0 -1,000 Sep-15 . Sep-16 Sep-17 Sep-18 Sep-19 Sep-20

Housing starts maintain their pace while enduring virus and rising prices.

Washington’s labor market melts down, moves toward recovery, and then... Eforts to slow the spread of COVID-19 quickly shut down much of the Washington economy in March through much of May 2020. Te restrictions associated with social distancing led to job losses that basically ballooned of the charts, and the word “unprecedented” became a standard part of an economist’s vocabulary. Consequently, the fgures presented by Figure 2-9 show the monthly change in nonfarm employment in the state beginning when restrictions were frst implemented in March, and then when they were being gradually relaxed starting in May and carrying through October.


Te next fgure might look like the employment situation of other states, as all 50 states sufered severe job losses in April. Washington’s labor market appeared to have bottomed out sometime in May as the state began to ease restrictions. Employment bounced back in June with enough forward momentum to carry strong gains into July and August. Afer the initial recovery boost, employment growth appeared to be slowing. Te virus still lingered, and businesses still needed to operate with social distance caution and limited capacity. To date in October 2020, the state has been able to recapture about 60 percent of the jobs lost from March through May.

As the year moved closer to winter, reported COVID-19 cases began escalating, prompting a reissuing of restrictions on businesses. Payroll employment growth is now threatened by this latest round, and it runs the risk of turning negative in the months of December and January. Te discovery of vaccines for the disease has created hope that the virus can be placed under control. New rounds of fscal stimulus coming with Congressional legislation can hopefully buy enough time for the economy to hold steady while the vaccine distribution process takes, but as has been the case for most of this year, the economy will be tied to the evolution of the virus and policymakers’ strategies for managing it.

Figure 2-9. Mionthly employment change, seasonally adjusted Washington state, March 2020 through October 2020 Source: Employment Security Department/LMEA; U.S. Bureau of Labor Statistics, Current Employment Statistics

100,000

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Monthly employment has shifted dramatically with respect to policy instituted to deal with the pandemic.




Figure 2-10 shows how payroll employment has fared by industry over the course of the year from October 2019 through October 2020. As in most states, leisure and hospitality accounted for the vast majority of job losses. To date, employment has been slow to recover with only about half of the workers let go during the lockdown rehired. Another industry that requires a high degree of customer contact or interaction, other services, is also having trouble getting employment back on track. Manufacturing is another industry that was particularly impacted by the virus. Most of the fallout is contained in aerospace product and parts manufacturing. Boeing had already been shedding jobs due to problems with its 737 Max, and the pandemic rapidly decreased air travel.

By contrast, hiring has held up well in information, which has beneftted from a shif to remote work. Retail trade hiring has been mixed with many sectors shifing to provide more online services at the expense of customer services at brick and mortar stores. Warehouse and grocery stores have had more demand for products during the pandemic and have added workers as a result. Hiring has also snapped back in construction. Professional and business services has also recovered jobs to the extent which employment in the industry is only marginally changed over the course of the year.

Figure 2-10. Percent change in nonfarm employment by industry sector Washington state, October 2019 through October 2020 Source: Employment Security Department/LMEA; U.S. Bureau of Labor Statistics, Current Employment Statistics

Information Retail trade

Construction Professional and business services

Transportation, warehousing and utilities Financial activities

Education and health services Total nonfarm

Mining and logging Government

Wholesale trade Manufacturing Other services

Leisure and hospitality

Indu

strie

s

-24% -20% -16% -12% -8% -4% 0% 4%

Employment is still down in most industries over the course of the year from October 2019 through October 2020.


National, state and local unemployment rates bounce due to shifts in employment Washington state’s unemployment rate had been steadily declining as employment growth continued during the economic expansion. By February 2020, the state’s unemployment rate had sunk to a historical series low of 3.8 percent (Figure 2-11). Te Seattle Metropolitan Division (MD), a major hub of state economic activity, saw its unemployment rate fall from 3.8 percent in October 2016 to a new series low of 2.6 percent in February 2020.

While Washington state was busy setting records for low unemployment rates, other states were doing the same. Economically diverse states like California and New York recorded new unemployment lows in February 2020; California’s rate descending to 3.9 percent and New York’s coming in at 3.7. Tourism dependent states like Florida and Nevada were doing well economically with each state seeing record low unemployment rates. Florida’s rate reached a low of 2.8 percent in February 2020 while Nevada’s unemployment rate hit its low at 3.6 percent. Te national unemployment rate for the U.S. matched its 50-year low of 3.5 percent in February 2020. Troughout most of the expansion, the U.S. unemployment rate has usually been slightly lower than the Washington state unemployment rate.

Te aggressive mitigation eforts implemented by Washington and other states to slow the spread of COVID-19 caused economic activity to scale back sharply in March, causing the unemployment rate to quickly move up 1.3 points statewide and 3.0 points higher in greater Seattle to 5.6 percent. Job losses across the nation peaked in April, sending unemployment rates to their high-point zenith. Te U.S. unemployment rate soared to 14.7 percent, while Washington state’s rate reached 16.3 percent and Seattle’s rate moved to 14.5 percent. With leisure and hospitality industries experiencing the greatest share of job losses, states like Florida, Hawaii, and Nevada were hit hard. Nevada’s unemployment rate reached the highest in the country, coming in at 30.1 percent while Hawaii’s rate skyrocketed to 23.8 percent.

Unemployment rates have been falling since then as most states, including Washington, began easing restrictions. Since the economy has yet to fully recover, the rates remain elevated. Te unemployment rate for the U.S. dropped to 6.9 percent in October 2020. Washington’s rate has come back down to 6.0 percent, below the national rate. Seattle had an unemployment rate of 4.8 percent in October. Other states have experienced a similar decline. Two of the hardest hit states, Hawaii and Nevada, have lower unemployment rates, but those rates are still at the




higher end at 14.2 and 11.9 percent respectively. Two of the states that have had major trouble with the spread of COVID-19, California and New York, now have unemployment rates in the 9.0 percent range.

While its direction has been predictable given what has been happening to payroll employment, the magnitude of the changes in unemployment rates has been harder to determine and remains unpredictable. For now, the recent downward trend in unemployment rates may soon be disrupted as Washington and other states begin reissuing gathering restrictions to combat recently rising COVID-19 cases. How long this may continue will rest with society’s ability to tame the virus.

Figure 2-11. Monthly seasonally adjusted unemployment rates United States, Washington state and Seattle MD, October 2016 through October 2020 Source: Employment Security Department/LMEA; U.S. Bureau of Labor Statistics, Local Area Unemployment Statistics

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-,~ \

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J

~-~------------,------~-----•

- - ---

Unem

ploy

ment

rate

18%

16%

14%

12%

10%

8%

6%

4%

2%

% Washington U.S. Seatt le

Oct-16 Oct-17 Oct-18 Oct-19 Oct-20

Unemployment rates skyrocketed when business restrictions were implemented but have come down when some restrictions were eased.


Chapter 3: Seasonal, structural and cyclical industry employment Te purpose of this chapter is to identify the most infuential factors in employment trends for diferent industries in Washington state. Te results inform a better understanding of current employment trends and practical applications such as job placement, unemployment insurance and training programs. Annually, industries with high levels of seasonality experience signifcant variation in monthly employment. With this monthly variation, short-term high job demand follows upon employment declines. For industries with high cyclical variation, periods of booming employment can be followed by periods of decline. Training programs should be developed in anticipation of such variations.

Te relationships between industry and total state employment are also analyzed (Appendix 2). Te results of this analysis can help create a better understanding of key state employment trend components.

Our analysis is based on historical employment data from January 2002 through December 2019.2 Te analysis splits industry employment trends among the following four components:

1. Seasonal: regular and predictable employment changes that recur each calendar year, caused by seasonal factors, which can include natural factors (changes in weather), administrative measures (starting and ending of the school year) and social, cultural or religious traditions (fxed holidays such as New Year’s Day).

2. Trend: shifs in long-term employment growth trends driven by fundamental structural change and productivity trends in industries, rather than the cyclical fuctuations in employment. Structural changes in employment can be initiated by productivity improvement, policy changes or permanent changes in resources, technology or society. Technological innovation has introduced entirely new industries and caused other industries to decline. In addition, it has reshaped the entire labor market through increased efciencies, such as automated manufacturing, data collection and analysis and communications.

3. Cyclical: employment cchanges attributed to the business cycle in general or specifc events such as the housing bubble bursting in 2007 or cyclical variation in aerospace employment.

4. Irregular: random employment changes not picked up by regular seasonal and cyclical components (e.g., non-regular seasonality, weather variation and labor strikes).

2 Historical data for employment covered by the unemployment insurance system was categorized by NAICS (North American Industrial Classification System) code, at the 3-digit code level. Altogether, the historical time series data included 95 industries and one series for total employment.


Chapter 3 Seasonal, structural and cyclical industry employment

Seasonal industries Te analysis this year showed that of 95 industries in Washington state, 18 have high levels of seasonality with a seasonal factor3 over 4.0 percent. Crop production, scenic and sightseeing transportation, and support activities for agriculture and forestry were the most seasonal industries (Figure 3-1).

Figure 3-1. Industries with high levels of seasonality Washington state, 2002 to 2019 Source: Employment Security Department/LMEA; U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages

NAICS Industry Seasonal factor 111 Crop production 31.61% 487 Scenic and sightseeing transportation 20.37% 115 Support activities for agriculture and forestry 17.09% 525 Funds, trusts and other financial vehicles 14.23% 213 Support activities for mining 11.85% 711 Performing arts, spectator sports and related industries 10.56% 237 Heavy and civil engineering construction 7.38% 814 Private households 6.55% 114 Fishing, hunting and trapping 5.71% 721 Accommodation 5.35% 492 Couriers and messengers 5.30% 316 Leather and allied product manufacturing 5.27% 519 Other information services 5.17% 312 Beverage and tobacco product manufacturing 5.08% 311 Food manufacturing 4.40% 448 Clothing and clothing accessories stores 4.28% 713 Amusement, gambling and recreation industries 4.25% 512 Motion picture and sound recording industries 4.12%

Crop production, scenic and sightseeing transportation and support activities for agriculture and forestry have been the industries with the highest degree of seasonality in Washington state.

Structural and cyclical industries Annual totals of seasonal, irregular and cyclical components represent a statistically insignifcant share of employment. Cyclical is balanced between years, while seasonal and irregular are balanced within a year. For annual trends, the combination of the trend and cycle components represents virtually all total employment changes.

3 See Appendix 2 for seasonal factor definition.

Seasonal, structural and cyclical industry employment Chapter 3

For total covered employment, the trend component accounts for 79.7 percent of total employment changes (Appendix fgure A2-2). Tere were 29 industries where the structural (trend) component accounted for at least two-thirds of the change in employment (Figure 3-2). Ambulatory healthcare services, local government (other), educational services, and beverage and tobacco product manufacturing were the most highly infuenced by the trend factor, and consequently less by the cyclical factor. Te trend component contributed relatively more to these four industries than to employment changes for total nonfarm employment. All other industries have lower trend contributions than total nonfarm employment.

Figure 3-2. Industries most influenced by structural factors Washington state, 2002 to 2019 Source: Employment Security Department/LMEA; U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages

NAICS Industry Structural factor 621 Ambulatory healthcare services 91.41% 903 Local government (other) 82.71% 611 Educational services 82.58% 312 Beverage and tobacco product manufacturing 81.33% 722 Food services and drinking places 78.31% 812 Personal and laundry services 78.06% 519 Other information services 77.15% 531 Real estate 76.75% 425 Wholesale electronic markets and agents and brokers 76.39% 238 Specialty trade contractors 75.17% 453 Miscellaneous store retailers 74.71% 532 Rental and leasing services 74.60% 541 Professional, scientific and technical services 74.26% 511 Publishing industries (except Internet) 73.27% 454 Nonstore retailers 73.09% 236 Construction of buildings 72.97% 622 Hospitals 72.78% 444 Building material and garden equipment and supplies dealers 71.63% 813 Religious, grantmaking, civic, professional and

similar organizations 70.61%

551 Management of companies and enterprises 70.12% 481 Air transportation 69.94% 561 Administrative and support services 69.62% 311 Food manufacturing 69.43% 237 Heavy and civil engineering construction 69.26% 452 General merchandise stores 68.89%




NAICS Industry Structural factor 337 Furniture and related product manufacturing 68.22% 113 Forestry and logging 67.27% 322 Paper manufacturing 66.90% 522 Credit intermediation and related activities 66.15%

These Washington industries have been most influenced by structural factors such as technology changes, policy changes and changing demographics.

For 15 industries, the cyclical component accounted for more than half of the change in employment in the indicated industries (Figure 3-3). For total covered employment, the cyclical component accounted for 20.3 percent of total employment change. Support activities for mining, oil and gas extraction and scenic and sightseeing transportation were the most highly infuenced by the cyclical factor, and consequently less by the structural (trend).

Figure 3-3. Industries most influenced by cyclical factors Washington state, 2002 to 2019 Source: Employment Security Department/LMEA; U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages

NAICS Industry Cyclical factor 213 Support activities for mining 67.19% 211 Oil and gas extraction 60.93% 487 Scenic and sightseeing transportation 60.40% 486 Pipeline transportation 60.37% 525 Funds, trusts and other financial vehicles 57.77% 114 Fishing, hunting and trapping 55.36% 315 Apparel manufacturing 55.31% 324 Petroleum and coal products manufacturing 54.67% 316 Leather and allied product manufacturing 54.60% 443 Electronics and appliance stores 54.38% 111 Crop production 54.20% 491 Postal service 53.96% 482 Rail transportation 51.57% 562 Waste management and remediation services 50.88% 333 Machinery manufacturing 50.51%

These Washington industries have been most sensitive to cyclical movements and have exhibited shifts of relatively rapid employment growth and decline.

Seasonal, structural and cyclical industry employment Chapter 3

See Appendix 2 for a description of the statistical methodology used to categorize and measure the major factors behind employment change by industries, and Appendix fgures A2-2 and A2-3 with the full results of these analyses.

In summary, training providers and other planners need to be aware that not every upswing in employment is an indication of an increase in demand. Te upswing may simply be annual seasonal fuctuations or cyclical fuctuations.



Chapter 4: Unemployment Tis chapter discusses unemployment benefts and unemployment rates.

Unemployment benefits

In September 2020, more than 553,289 people received unemployment payments for all beneft entitlements. For comparison, during the height of the great recession, January 2010, 305,086 people had received unemployment benefts. Figure 4-1 shows the number of monthly benefciaries in Washington state from January 2016 through September of 2020. Tese individuals received at least one payment of unemployment benefts under regular unemployment compensation, Pandemic Unemployment Assistance (PUA), Pandemic Emergency Unemployment Compensation (PEUC), or the Extended Benefts program (EB). Te number of benefciaries in 2020 increased signifcantly starting in March 2020 with the COVID-19 pandemic. In April 2020, the number of paid claims increased by nearly three times. Te number of claimants receiving benefts peaked at 711,945 in May 2020.

Figure 4-1. Unemployment benefit recipients by month, all benefits4

Washington state, January 2016 through September 2020 Source: Employment Security Department/LMEA, Unemployment Insurance Data Warehouse

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The number of Washingtonians receiving unemployment benefits as of September 2020 was 553,289.

4 All benefit programs include regular unemployment compensation, Pandemic EmergencyUnemployment Compensation (PEUC), Pandemic Unemployment Assistance (PUA) and Extended Benefits (EB).


Chapter 4 Unemployment

Duration of unemployment benefits

Typically, workers covered by unemployment insurance can receive up to 26 weeks of regular unemployment benefts in a 52-week beneft year. Te 52-week beneft year begins when an individual applies for unemployment benefts.

More weeks of unemployment benefits available after the recession Because of the extraordinary steep loss of jobs related to the COVID-19 pandemic, additional weeks of federally funded unemployment benefts were made available to unemployed workers afer they used all their regular unemployment benefts. During 2020, regular unemployment claimant were able to receive up to a total of 59 weeks of benefts: 26 weeks of state benefts, 13 weeks of PEUC benefts and 20 weeks of EB (PUA recipients were able to receive up to 46 weeks into December 2020).

Figure 4-2 compares the average duration of benefts in Washington state for those who were receiving only regular benefts (up to 26 weeks) to the duration of all benefts, that includes regular benefts, PEUC and EB benefts. Te average duration information is calculated by looking at the number of weeks compensated for a 12-month period divided by the number of frst payments. Because of the surge in frst payments of regular benefts payments in April of 2020, and delays in compensating claimants between April and May of 2020, we saw a drop in the overall average duration of benefts in April and May of 2020.

Te annual average duration for regular benefts and all benefts peaked in 2010 at 20.7 weeks and 42 weeks, respectively. In 2011, average duration of regular benefts declined to 17.9 weeks and 39.5 weeks for all benefts. Te average duration of both regular benefts and all benefts in 2017 was 14.8. From January 2018 through September 2020, the average duration for both regular benefts and all benefts has been increasing going from an average duration of 14.7 weeks in January 2018 to 16.8 weeks as of September 2020 for regular beneft entitlements, and 18.2 weeks for all beneft entitlements.5

Federal extensions of benefits were passed in March of 2020. Regular, PEUC, and EB are included here. Since March 2020, claimants could receive up to 56 weeks of total benefit payments.

5

Unemployment Chapter 4

Figure 4-2. Average duration of regular unemployment benefits compared to all benefits Washington state, January 2000 through September 2020 Source: Employment Security Department/LMEA, Unemployment Insurance Data Warehouse

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0 Regular benefits All benefits

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Shaded areas are U.S. recession periods.

The average duration of benefits as of September 2020 is 16.9 weeks for regular benefit entitlements and 18.2 weeks for all entitlements.

Benefit exhaustions have been increasing

Unemployed individuals exhaust their benefts when they have received all regular unemployment benefts, PEUC and EB available to them. Te following exhaustion analysis will focus only on the claimants that have exhausted regular benefts between October 2019 and September 2020. Between January 2019 and April 2020, only regular beneft entitlements were available. In March 2020, the CARES Act (federal stimulus) was passed by Congress and it included the Pandemic Emergency Unemployment Compensation (PEUC) program, providing an extra 13 weeks of benefts to regular unemployment claimants. Washington state triggered onto extended benefts on April 18th, providing 13 additional weeks of unemployment benefts. A high extended beneft period was triggered on June 7th, providing seven additional weeks of extended benefts for a total of 20 weeks of extended benefts.

Figure 4-3 shows the monthly exhaustions for Washington state regular unemployment benefts. Te level of exhaustions of regular claims has been increasing since March 2020, with substantial increases in the number of exhaustions of regular unemployment benefts occurring in September 2020 (60,158 regular beneft exhaustions).




Figure 4-3. Number of people exhausting regular unemployment benefits Washington state, January 2010 through September 2020 Source: Employment Security Department/LMEA, Unemployment Insurance Data Warehouse

~ I

~ ... --~ - ) -... -- ...... -- -0

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In September 2020, 60,158 people exhausted their regular unemployment benefits.

Benefit exhaustions by industry, occupation and area Higher levels of beneft exhaustions are generally associated with long-term unemployment. Te following fgures detail patterns of beneft exhaustions by industry, occupation and location.

Exhaustions by industry Figure 4-4 presents exhaustions by industry from September 2019 to September 2020. To provide further context, the fgure also includes each industry’s percent of total nonfarm employment and exhaustion-to-employment ratio. Te exhaustion-to-employment ratio can be used to identify industries characterized by long-term unemployment and that continue to struggle in their recovery from the last recession.

From October 2019 through September 2020, workers in the arts, entertainment and recreation sector were most likely to exhaust regular unemployment benefts with an exhaustion-to-employment ratio of 8.9 percent. Workers in the educational service sector were the next most likely to exhaust regular unemployment benefts with an exhaustion-to-employment ratio of 8.7 percent.

I I I I I


Te accommodation and food services sector accounted for the greatest portion of regular beneft exhaustions at 18.9 percent. Te manufacturing and construction industries’ share of total covered employment was 8.4 percent and 6.0 percent, respectively; the exhaustion-to-employment ratio for those sectors was 3.4 and 6.3, respectively. Healthcare and social assistance represented 12.6 percent of exhaustions.

Figure 4-4. Unemployment benefit exhaustions by industry, regular benefits Washington state, October 2019 through September 2020 Source: Employment Security Department/LMEA, Unemployment Insurance Data Warehouse; U.S. Bureau of Labor Statistics, Current Employment Statistics

NAICS Industry sector

Annual exhaustions,

regular benefits

Percent of all

exhaustions

Industry share of nonfarm

employment

Exhaustions-to-employment

ratio 72 Accommodation and food services 24,032 18.9% 8.4% 8.4% 23 Construction 13,029 10.2% 6.0% 6.3% 44 - 45 Trade 12,651 9.9% 11.2% 3.3% 31 - 33 Manufacturing 9,881 7.8% 8.4% 3.4% 62 Healthcare and social assistance 9,878 7.8% 12.6% 2.3% 56 Administrative and support and waste management and

remediation services 9,197 7.2% 5.0% 5.4%

Unknown 6,021 4.7% N/A N/A 81 Other Services 5,603 4.4% 3.0% 5.5% 54 Professional, scientific and technical services 5,587 4.4% 6.1% 2.7% 42 Wholesale trade 4,869 3.8% 3.9% 3.6% 71 Arts, entertainment and recreation 4,796 3.8% 1.6% 8.9% 48 - 49 Transportation and warehousing 4,247 3.3% 3.1% 4.0% 61 Educational services 3,927 3.1% 1.3% 8.7% 11 Agriculture, forestry, fishing and hunting 3,769 3.0% 3.0% 3.7% 51 Information 3,173 2.5% 4.2% 2.2% 53 Real estate, rental and leasing 2,603 2.0% 1.6% 4.7% GOV Government 1,821 1.4% 16.5% 0.3% 52 Finance and insurance 1,703 1.3% 2.8% 1.8% 55 Management of companies and enterprises 233 0.2% 1.3% 0.5% 21 Mining 180 0.1% 0.1% 8.0% 22 Utilities 166 0.1% 0.2% 3.2%

Total 127,366 100.0% 100.0% 3.7%

N/A = Nonfarm employment and does not include farmworkers, private households or non-profit organization employees. Exhaustion totals were not comparable to nonfarm employment totals. *The majority of workers in “unknown” industries were a product of out-of-state employers. Washington State Employment Security Department is unable to identify industries where the primary employer is out of state.

Arts, entertainment and recreation workers were most likely to exhaust unemployment benefits from October 2019 through September 2020 with an 8.9 exhaustion-to-employment ratio.




Exhaustions by occupation Figure 4-5 examines unemployment beneft exhaustions by occupational group. Management, construction and extraction, and ofce and administrative support occupations combined accounted for over 40 percent of all exhaustions. Since total covered employment is reported only by industry and not by occupation, each occupation’s percent of total covered employment and exhaustion-to-employment ratio were not available to be included in Figure 4-5.

Figure 4-5. Unemployment benefit exhaustions by major occupational groups, regular benefits Washington state, October 2019 through September 2020 Source: Employment Security Department/LMEA, Unemployment Insurance Data Warehouse

SOC Major occupational group Annual exhaustions, regular benefits Percent of all exhaustions 35 Food preparation and serving related 20,905 16.4% 11 Management 16,148 12.7% 47 Construction and extraction 12,661 9.9% 43 Office and administrative support 12,303 9.7% 41 Sales and related 9,858 7.7% 53 Transportation and material moving 8,053 6.3% 51 Production 7,692 6.0% 39 Personal care and service 5,734 4.5% 37 Building and grounds cleaning and maintenance 4,467 3.5% 13 Business and financial operations 3,747 2.9% 49 Installation, maintenance and repair 3,426 2.7% 25 Education, training and library 3,281 2.6% 45 Farming, fishing and forestry 3,145 2.5% 31 Healthcare support 2,792 2.2% 15 Computer and mathematical 2,684 2.1% 27 Arts, design, entertainment, sports and media 2,427 1.9% 17 Architecture and engineering 1,745 1.4% 29 Healthcare practitioners and technical 1,654 1.3% 33 Protective service 1,385 1.1% - Unknown 979 0.8% 21 Community and social services 887 0.7% 19 Life, physical and social science 788 0.6% 23 Legal 397 0.3% 55 Military specific 208 0.2%

Total 127,366 100.0%

Unemployed workers in food preparation and serving related occupations accounted for 16.4 percent of all individuals to exhaust regular unemployment benefits from October 2019 through September 2020.

I I I I


Exhaustions by workforce development area Figure 4-5 examines unemployment beneft exhaustions by occupational group. Management, construction and extraction, and ofce and administrative support occupations combined accounted for over 40 percent of all exhaustions. Since total covered employment is reported only by industry and not by occupation, each occupation’s percent of total covered employment and exhaustion-to-employment ratio were not available to be included in Figure 4-5.

From October 2019 through September 2020, workers in the Pierce County, Snohomish County, and South-Central Washington WDAs, were most likely to exhaust regular unemployment benefts with an exhaustion-to-employment ratio of 1.2.

Seattle-King County and Pierce County accounted for more than one-fourth of exhaustions at 30.4 and 11.3 percent, respectively. Seattle-King County’s and Pierce County’s share of total covered employment were 41.7 percent and 9.2 percent, respectively; the exhaustion-to-employment ratios were 0.7 and 1.2, respectively.

Seattle-King County accounted for the largest share of exhaustions and employment, but had the lowest exhaustion-to-employment ratio (0.7).

Figure 4-6. Unemployment benefit exhaustions by workforce development area, regular benefits Washington state, October 2019 through September 2020 Source: Employment Security Department/LMEA, Unemployment Insurance Data Warehouse ; U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages

Workforce development area Annual exhaustions,

regular benefits Percent of

exhaustions 2019 industry share of nonfarm employment

Exhaustions to employment ratio

Seattle-King County 38,660 30.4% 41.7% 0.7 Pierce County 14,347 11.3% 9.2% 1.2 Snohomish County 12,972 10.2% 8.5% 1.2 Out of state 10,946 8.6% N/A N/A Spokane County 7,602 6.0% 6.6% 0.9 Pacific Mountain 7,263 5.7% 5.5% 1.0 Southwest WA 7,251 5.7% 5.9% 1.0 Northwest WA 6,840 5.4% 4.9% 1.1 South Central WA 6,058 4.8% 4.1% 1.2 Benton-Franklin 4,515 3.5% 3.7% 1.0 Olympic 4,499 3.5% 3.6% 1.0 North Central WA 4,313 3.4% 3.6% 0.9 Eastern WA 2,100 1.6% 2.1% 0.8 Total 127,366 100.0% 100.0%

Seattle-King County accounted for the largest share of exhaustions and employment but had the lowest exhaustion-to-employment ratio (0.7).




Unemployment rate Te overall unemployment rate is a ratio of the estimated number of unemployed individuals looking for work divided by the civilian labor force. Te labor force is made up of individuals who are employed or who are actively seeking work. Tis is the most commonly used unemployment rate, and includes both workers covered by unemployment insurance and those who are not covered.6

We also present the insured unemployment rate, which is the ratio of the number of insured unemployed (those drawing unemployment benefts) divided by the total number of individuals (working and not working) covered by unemployment insurance.

Figure 4-7 compares the overall and insured unemployment rates for Washington. Te rates have basically moved in tandem. Te insured rate is historically about half the overall unemployment rate. In late 2008, both measures of unemployment began a dramatic rise, with rates peaking during the frst quarter 2010. However, since the onset of the COVID pandemic, the gap between the overall and insured unemployment rates has narrowed. Tis means there were increasing numbers of unemployed workers eligible for unemployment benefts.

Figure 4-7. Overall unemployment rate, seasonally and not seasonally adjusted and insured unemployment rate Washington state, January 2000 through September 2020 Source: Employment Security Department/LMEA; U.S. Bureau of Labor Statistics, Local Area Unemployment Statistics

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The gap between unemployed workers who are eligible for unemployment benefits and those who are narrowed following the most recent recession. 6 Workers covered by unemployment insurance are unemployed through no fault of their own, as

determined by state law. In order to qualify for this benefit program, they must have worked at least 680 hours in covered employment during the past 12 to 18 months. At least some of these hours must have been earned in Washington state. They must also be able to work and be available for work each week that they are collecting benefits.


The overall unemployment rate Te overall unemployment rate is widely used in economic analysis as a lagging indicator of the direction of the economy. As noted previously, the unemployment rate is a ratio of the estimated number of unemployed who are seeking work, divided by the labor force. Te labor force is limited to individuals who are employed or seeking work.

As shown in Figure 4-8, the state unemployment rate peaked in the second quarter of 2020 at 16.3 percent. During most of 2010, 2011, and 2012, the unemployment rate for Washington state remained higher than the national rate. Starting in August 2012, the state unemployment rate fell below the national rate and remained below the national rate through September 2014. It rose above the national rate in September 2014 at 6.0 percent. From September 2014 to June 2020, the state unemployment rate remained above the national rate. In April 2020, both the national and state unemployment rates increased by over 10 percentage points. Te state rate increased from 5.1 percent in March 2020 to 16.3 percent in April 2020. Te national unemployment rate increased from 4.4 percent in March 2020 to 14.7 percent in April 2020. By September 2020, the state and national rates were at 7.9 and 7.8 percent, respectively.

Te Seattle-Bellevue-Everett Metropolitan Division (MD) has reported a lower unemployment rate than the rest of Washington and the nation since 2004. However, during May and June of 2020 the Seattle MD experienced a higher unemployment rate than nation. Te unemployment rate in the Seattle MD increased from 2.6 percent in March 2020 to 14.5 percent by June of 2020. For comparison, the national unemployment rate increased from 4.4 percent in March 2020 to 11.1 percent in June of 2020.




Figure 4-8. Historical U-3 unemployment rates, seasonally adjusted United States and Washington state, January 2000 through September 2019 Source: Employment Security Department/LMEA; U.S. Bureau of Labor Statistics, Local Area Unemployment Statistics; National Bureau of Economic Research

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National and state unemployment rates followed similar patterns during the recent recession. From May 2020 through September 2020, the Seattle unemployment rate declined more rapidly than the Washington state unemployment rate.

Other measures of unemployment

Alternative unemployment rates Te U.S. Bureau of Labor Statistics (BLS) reports six alternative measures of labor underutilization. Te commonly used defnition of the unemployment rate, shown in Figure 4-8, is a ratio of the estimated number of unemployed who are seeking work, divided by the labor force. Tis is equivalent to what the BLS calls “U-3.”

A common criticism of the standard measurement of unemployment is that it is too narrow – for instance, it excludes individuals who are not working and would like to work, but have given up looking for work.

In response to criticism, the BLS has made available alternative measurements that are progressively more inclusive than the commonly reported unemployment rate. Te standard measurement (U-3), along with two of the six alternative measurements, are defned as:

• U-3 – Unemployed as a percent of the labor force.


• U-4 – Unemployed plus discouraged workers, as a percent of the labor force plus discouraged workers.

• U-6 – Unemployed plus all marginally attached workers and employees working part time for economic reasons, all as a percent of the labor force plus all marginally attached workers.

Te U-4 measure followed a similar pattern of decline in Washington state and the country as a whole coming out of the great recession (Figure 4-9). Te moving average for third quarter 2009 through second quarter 2010 had Washington state and the nation both at 10.3 percent. From second quarter of 2010 through the second quarter of 2020, the Washington state U-4 unemployment rate decreased to 4.3 percent while the nation’s rate decreased to 3.9 percent. Tis indicates that relatively more Washington residents had given up looking for work and had dropped out of the labor force during that period. Te Washington U-4 rate, for the third quarter 2019 through the second quarter 2020, is now 4.3 percent and the U.S. rate is 3.9 percent.

Figure 4-9. U-4 unemployment rate (includes discouraged workers), four-quarter moving average United States and Washington state, third quarter 2009 through second quarter 2020 Source: U.S. Bureau of Labor Statistics, Local Area Unemployment Statistics

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U.S. total unemployed and discouraged workers Washington total unemployed and discouraged workers

2009 Q3- 2011 Q3- 2012 Q3- 2014 Q3- 2016 Q3- 2017 Q4- 2018 Q2- 2018 Q4- 2019 Q2-2010 Q2 2012 Q2 2013 Q2 2015 Q2 2017 Q2 2018 Q3 2019 Q1 2019 Q3 2020 Q1

The U-4 measure of unemployment has been declining throughout the recovery. As of June 2020, Washington’s U-4 rate is currently 10.9. percent and the U.S. rate at 10.4 percent.

U-6 is the broadest measure of unemployment. Te gap between the U-6 and U-3 rates has narrowed to its lowest level post-recession. Tis demonstrates the decrease in the ranks of discouraged workers, marginally attached workers and those working part time involuntarily, even more




dramatically than the number of unemployed (Figure 4-10). Tis holds true for the state of Washington, where many underutilized workers are in the employed “part time for economic reasons” category. Washington’s U-6 four-quarter moving average unemployment rate has remained higher than the nation’s since 2014. Most recently, Washington’s U-6 rate remains 0.50 percentage points above the national rolling average from third quarter 2019 through second quarter 2020.

Figure 4-10. U-3 (standard) and U-6 (includes marginally attached workers and those working part time involuntarily) unemployment rates, four-quarter moving average United States and Washington state, third quarter 2009 through second quarter 2020 Source: U.S. Bureau of Labor Statistics, Current Population Survey, Local Area Unemployment Statistics

-•- --U-3

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2009 Q3- 2011 Q3- 2012 Q3- 2014 Q3- 2016 Q3- 2017 Q4- 2018 Q2- 2018 Q4- 2019 Q2-2010 Q2 2012 Q2 2013 Q2 2015 Q2 2017 Q2 2018 Q3 2019 Q1 2019 Q3 2020 Q1

The most broadly defined U-6 measure of unemployment for Washington remains above the national rolling average.

Chapter 5: Employment projections About the employment, industry and occupational projections Employment projections provide a general outlook for industry and occupational employment in Washington state. Tey provide job seekers, businesses, policymakers and training providers an idea of how much an industry or occupation is projected to change over time and show the future demand for workers.

On an annual basis, the Employment Security Department (ESD) produces industry employment projections for two, fve and 10 years from a base period. For this annual report, the base period for the two-year (short-term) projections is second quarter 2019. Te base period for both the fve-year (medium-term) and 10-year (long-term) projections is 2018.

Stafng patterns show proportional compositions of occupations within industries and are used to convert industry projections into occupational projections.

Industry classifcations are based on the North American Industry Classifcation System (NAICS). However, they have been modifed to match industry defnitions used by the U.S. Bureau of Labor Statistics’ (BLS) Occupational Employment Statistics (OES) program. Tese modifed industry defnitions are called Industry Control Totals (ICTs). Te Standard Occupational Classifcation (SOC) system is used to group occupations. Appendix 4 contains frequently asked questions relating to projections. Appendix 7 provides a glossary of terms.

Data sets used to develop projections Te following data sets are used to produce projections:

1. Historical employment time series, consisting of U.S. Bureau of Labor Statistics’ Quarterly Census of Employment and Wages (QCEW) data.

2. Employment not covered by the unemployment insurance system from the U.S. Bureau of Labor Statistics’ Current Employment Statistics (CES) program.

3. Occupational employment by industries (stafng patterns) based on an OES survey.

4. National data for self-employed ratios, change factors, etc.

5. Independent variables (predictive indicators), which help to project the future direction of the economy, from IHS Global Insight’s national forecast.

Employment Security Department January 2021 2020 Labor Market and Economic Report Page 57

Chapter 5 Employment projections

Use of employment projections Employment projections are intended for career development over time, not as the basis for budget or revenue projections, or for immediate corrective actions within the labor market.

Employment projections are the basis of the Occupations in Demand (OID) list covering Washington’s 12 workforce development areas (WDAs) and the state. Te OID list is used to determine eligibility for a variety of training and support programs but was created to support the unemployment insurance Training Benefts Program. Appendix 4 contains a technical description of the OID list.

Te full OID list is accessible through the “Learn about an occupation” tool located on the ESD labor market information webpage at: https:// esd.wa.gov/labormarketinfo/learn-about-an-occupation#/search

Tis chapter highlights fndings on specifc aspects of Washington’s employment outlook. In the frst section, industry projections results, we describe changes in employment by industry from 2018 to 2028. In the second section, occupational projections results, we look at:

• Major occupational groups • Specifc occupations

Detailed information on the projected demand for industry and occupational employment is available in the Employment Projections data fles at: https://esd.wa.gov/labormarketinfo/projections

In addition, detailed skill projections information is available in Appendix 5 of this report.

Te formal description of industry and occupational projection processes is presented in the 2019 Employment Projections Technical Report. Te technical report can be found at the data fles link above.

Key findings Te 10-year average annual growth rate for total nonfarm employment for the 2018 to 2028 period is projected to be 1.37 percent. Tis is a decrease from the 1.51 percent average annual growth rate predicted last year for 2017 to 2027.

Industry projections • Te largest increases by share of employment is projected for the

information sector and other services sector. • Te largest decreases by shares of employment are projected for

the manufacturing sector and construction sector.

January 2021 Employment Security Department Page 58 2020 Labor Market and Economic Report

https://esd.wa.gov/labormarketinfo/learn-about-an-occupation#/search

https://esd.wa.gov/labormarketinfo/learn-about-an-occupation#/search

https://esd.wa.gov/labormarketinfo/projections

Employment projections Chapter 5

Occupational projections Major occupational groups

• Te largest increases by shares of employment are projected for the computer and mathematical occupations.

• Te largest decreases by shares of employment are projected for the construction and extraction occupations.

• Te largest employment shares in 2028, from largest to smallest, are projected for the ofce and administrative support occupations, sales and related occupations and food preparation and serving-related occupations. As was the case in last year’s projections report, the frst two occupational groups are projected to have declining employment shares.

Two approaches to occupational job openings

A separations approach is based on BLS national rates. An alternative approach is based on job opening rates specifc to Washington state. Te separations method does not track job openings created by turnover when workers stay within an occupation, but change employers, while the alternative method attempts to track these openings.

Te separations and alternative data are available in the Occupational Projections data fles at: https://esd.wa.gov/labormarketinfo/projections.

Information about the separations methodology is available at: https:// www.bls.gov/opub/mlr/2018/article/occupational-separations-a-new-method-for-projecting-workforce-needs.htm. Information about the alternative methodology is available on our projections landing page at: https://esd.wa.gov/labormarketinfo/projections.

• For the separations method, sofware developer occupations are projected to have the largest number of average annual total openings.

• For the alternative method, retail salespersons occupations are projected to have the largest number of average annual total openings.

• For both separations and alternative occupations, no growth openings exceeded turnover openings.

• Totals of job openings caused by alternative turnover are about 25 times greater than openings due to growth, while totals of job openings caused by separations turnover are about ten times greater than openings due to growth.



https://www.bls.gov/opub/mlr/2018/article/occupational-separations-a-new-method-for-projecting-workforce-needs.htm





2020 industry projections results Figure 5-1 presents 2018 estimated employment, and 2018, 2023 and 2028 employment shares, and changes in employment shares from 2018 to 2023, 2023 to 2028 and 2018 to 2028 by industry for Washington state.

Trough 2028, the three industry sectors with the largest increases in employment shares are projected to be other services, information and health services and social assistance.7

For this same time period, the industry sector with the largest decrease in employment shares is manufacturing. Te second and third largest decreases are construction and wholesale trade.

Figure 5-1. Base and projected nonfarm industry employment Washington state, 2018, 2023 and 2028 Source: Employment Security Department/LMEA; U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages

Industry sector*

Est. empl. 2018

Est. empl.

shares 2018

Est.. empl.

shares 2023

Est. empl.

shares 2028

Percentage point change

in empl. shares

2018-2023


in empl. shares

2023-2028


in empl. shares

2018-2028 Natural resources and mining 6,400 0.19% 0.16% 0.15% -0.02% -0.01% -0.04% Construction 213,000 6.26% 6.07% 5.84% -0.19% -0.23% -0.42% Manufacturing 286,800 8.43% 8.08% 7.75% -0.35% -0.32% -0.68% Wholesale trade 136,200 4.00% 3.79% 3.67% -0.21% -0.12% -0.33% Retail trade 386,100 11.35% 11.06% 11.04% -0.29% -0.01% -0.30% Utilities 5,000 0.15% 0.14% 0.13% -0.01% -0.01% -0.02% Transportation and warehousing 109,400 3.22% 3.14% 3.09% -0.07% -0.05% -0.12% Information 133,900 3.94% 4.40% 4.52% 0.46% 0.13% 0.59% Financial activities 157,400 4.63% 4.51% 4.39% -0.12% -0.11% -0.23% Professional and business svcs. 427,100 12.55% 12.65% 12.80% 0.10% 0.14% 0.24% Education services 62,900 1.85% 1.90% 1.99% 0.05% 0.09% 0.14% Health svcs. and social assistance 427,000 12.55% 12.74% 13.04% 0.19% 0.31% 0.49% Leisure and hospitality 341,800 10.05% 10.18% 10.48% 0.14% 0.29% 0.43% Other services 121,300 3.57% 4.30% 4.30% 0.73% 0.00% 0.73% Federal government 74,800 2.20% 2.05% 1.93% -0.15% -0.12% -0.27% State and local gov. (incl. educ.) 513,100 15.08% 14.82% 14.86% -0.26% 0.04% -0.22%

*The sectors presented in the table are based on CES definitions.

The largest growth sectors for the state are projected for professional and business services and health services and social assistance.

7 All tables contain values that are calculated and then rounded. As a result, details might not always add up to totals.



Historical and projected growth rates Figure 5-2 shows the historical and projected growth rates for the state and Washington’s 12 workforce development areas (WDAs). Figure data are sorted on the projected growth rate 2018-2028 column.

Tree of the 12 WDAs have 2018 through 2028 projected growth rates greater than the previous 10 years’ growth, and nine have projected growth less than the previous 10 years’ growth. Seattle-King County has the highest projected growth rate of 1.72 percent with statewide second at 1.37 percent. Te statewide projected growth rate is 0.33 percentage points less than the 2008 through 2018 historical growth rate.

Te three WDAs with projected growth greater than the past are: Northwest, Spokane and Olympic.

Te largest positive diference between historical growth rates and projected growth rates is in the Spokane WDA. For the Spokane WDA, the diference between the historical and projected rates is 0.14 percentage points. Te Olympic WDA has the second-largest positive increase of 0.13 percentage points.

Even though the Benton-Franklin WDA has the largest negative diference between projected and historical rates of all WDAs and the state, it has the third-highest projected growth rate of 1.23 percent.

Te last column in Figure 5-2 represents the long-term growth rate on the historical linear trend line on all available historical data. Variances between long-term trend line rates and projected growth rates show the efects of the most recent changes in local employment trends. Tese variances may refect diferences in cyclical behavior.



Figure 5-2. Historical and projected total nonfarm employment growth Washington state and workforce development areas, 1990 to 2018 and 2018 to 2028 Source: Employment Security Department/LMEA; U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages

Workforce development area1 Historical growth rate2

2008-2018 Projected growth rate

2018-2028 Historical trend line growth rate3

1990-2018 Seattle-King County 1.97% 1.72% 1.36% Washington state 1.70% 1.37% 1.51% Benton-Franklin 2.56% 1.23% 2.29% Southwest 2.07% 1.18% 1.84% Tacoma-Pierce 1.61% 1.17% 1.76% Northwest 1.13% 1.15% 1.75% Spokane 1.00% 1.14% 1.27% Eastern 1.18% 1.07% 1.00% North Central 1.77% 1.07% 1.40% Snohomish 1.52% 1.03% 2.14% Pacific Mountain 1.19% 1.01% 1.34% South Central 1.30% 1.00% 0.91% Olympic 0.84% 0.97% 1.13%

1Workforce development areas are regions within Washington state with economic and geographic similarities. 2Historical growth is based only on covered employment. 3Historical trend growth is defined as the growth rate of the linear trend line.

Nine of the 12 WDAs have a projected growth rate less than the previous 10 years’ growth.

2020 occupational projections results Figure 5-3 shows major occupational group employment estimates and employment shares for Washington state.

At the state level, as was the case in last year’s report, one occupational group stands out with increases in employment shares from 2018 to 2028. Computer and mathematical occupations are projected to increase employment shares by 0.61 percentage points. Te next highest increase in shares is projected for management occupations, with an increase of 0.33 percentage points.

Te three largest decreases in employment shares at the state level are: sales and related occupations, 0.61 percentage points, production occupations, 0.50 percentage points, and construction and extraction occupations, 0.44 percentage points.



By 2028, the top three state occupational groups for shares of employment are projected to be:

1. Ofce and administrative support occupations (10.52 percent) 2. Sales and related occupations (8.73 percent) 3. Food preparation and serving related occupations (7.33 percent)

Figure 5-3. Base and projected occupational employment Washington state, 2018 to 2028 Source: Employment Security Department/LMEA; U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages, Occupational Employment Statistics

2-digit SOC Major occupational group

Est. empl. 2018

Est. empl.

shares 2018

Est. empl.

shares 2023

Est. empl.

shares 2028


in empl. shares

2018-2023


in empl. shares

2023-2028 11-0000 Management 226,457 5.92% 6.10% 6.25% 0.18% 0.16% 13-0000 Business and financial operations 253,233 6.62% 6.77% 6.91% 0.15% 0.13% 15-0000 Computer and mathematical 198,214 5.18% 5.58% 5.79% 0.40% 0.21% 17-0000 Architecture and engineering 85,982 2.25% 2.21% 2.15% -0.04% -0.05% 19-0000 Life, physical, and social science 43,508 1.14% 1.15% 1.15% 0.01% 0.00% 21-0000 Community and social service 60,901 1.59% 1.76% 1.75% 0.17% -0.01% 23-0000 Legal 29,884 0.78% 0.78% 0.77% -0.01% -0.01% 25-0000 Education, training, and library 209,714 5.48% 5.58% 5.69% 0.10% 0.11% 27-0000 Arts, design, entertain. sports and media 78,429 2.05% 2.15% 2.18% 0.10% 0.03% 29-0000 Healthcare practitioners and technical 180,624 4.72% 4.80% 4.92% 0.08% 0.12% 31-0000 Healthcare support 153,568 4.01% 4.10% 4.23% 0.09% 0.12% 33-0000 Protective service 67,891 1.77% 1.77% 1.77% -0.01% 0.00% 35-0000 Food preparation and serving related 268,838 7.03% 7.14% 7.33% 0.11% 0.19% 37-0000 Building and grounds cleaning and maint. 121,902 3.19% 3.29% 3.37% 0.10% 0.08% 39-0000 Personal care and service 116,093 3.03% 3.10% 3.16% 0.06% 0.06% 41-0000 Sales and related 357,349 9.34% 8.95% 8.73% -0.39% -0.22% 43-0000 Office and administrative support 412,045 10.77% 10.67% 10.52% -0.10% -0.15% 45-0000 Farming, fishing and forestry 100,477 2.63% 2.40% 2.33% -0.23% -0.07% 47-0000 Construction and extraction 237,916 6.22% 6.01% 5.78% -0.21% -0.23% 49-0000 Installation, maintenance and repair 150,151 3.92% 3.83% 3.72% -0.10% -0.11% 51-0000 Production 205,170 5.36% 5.10% 4.86% -0.26% -0.23% 53-0000 Transportation and material moving 267,171 6.98% 6.78% 6.65% -0.20% -0.13%

At the state level, computer and mathematical occupations stand out for their increase in employment shares.



By 2028 combined, these three major groups are projected to represent 26.58 percent of total employment shares for the state.

Te projected average annual growth rates for the major occupational groups in Washington state are presented in Figure 5-4. Computer and mathematical occupations (2.48 percent), community and social service occupations (2.31 percent), and art, design, entertainment, sports and media occupations (1.97 percent) are projected to grow faster than other occupational groups from 2018 to 2028.

In the long term, seven occupational groups are projected to fall below a 1.00 percent average annual growth rate: architecture and engineering (0.90 percent), transportation and material moving (0.85 percent), installation, maintenance and repair (0.80 percent), sales and related (0.66 percent), construction and extraction (0.60 percent), production (0.36 percent), and farming, fshing and forestry (0.15 percent).

Figure 5-4. Projected average annual growth rates for major occupational groups Washington state, 2018 to 2028 Source: Employment Security Department/LMEA; U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages, Occupational Employment Statistics

--

Computer and mathematical Community and social service

Arts, design, entertainment, sports and media Building and grounds cleaning and maintenance

Management Healthcare support

Business and financial operations Food preparation and serving related

Healthcare practitioners and technical Personal care and service

Education, training and library Life, physical and social science

Total, all occupations Protective service

Legal Office and administrative support

Architecture and engineering Transportation and material moving Installation, maintenance and repair

Sales and related Construction and extraction

Production Farming, fishing and forestry

Maj

or o

ccup

atio

nal g

roup

s

0% 1% 2%

Average annual growth rate 2018 to 2028

Computer and mathematical, personal care and service and health support occupations are projected to experience the largest growth rates from 2017 to 2027 (2.85, 2.15 and 2.11 percent, respectively).



Separations and alternative job openings Te Bureau of Labor Statistics (BLS) separations method measures job openings created by workers who leave occupations and need to be replaced by new entrants. In this method, workers who exit the labor force or transfer to an occupation with a diferent Standard Occupational Classifcation (SOC) are identifed as generating separation openings at the national level. Tis method does not track turnover within occupations. Turnovers within occupations occur when workers stay in occupations but change employers. Tis also means that under the BLS method, jobs flled by interstate movement when workers stay within occupations, are not identifed as new jobs.

Beginning with the 2017 projections cycle, ESD created a new Washington state specifc alternative occupational method to the BLS separations method. Te objective was to track job openings that occur when workers transfer within occupations. For simplicity, we refer to this method as the alternative method and to the rates as the alternative rates. While the alternative method can be used for any states that have useable wage fles, the alternative results are based on Washington state wage records, making them specifc to Washington state.

Te alternative rates track openings created by turnover within occupations (i.e., workers stay within occupations but transfer to diferent companies) and when workers leave one occupation for another or leave the workforce.

Te method consists of three major steps:

1. Estimating the total number of annual industry transfers that include: a. Transfers between industries b. Transfers inside industries c. New individuals in Washington state wage records (wage fle) d. Exits or individuals who are no longer in the wage fle

2. Converting industry transfers to occupational transfers using occupation-to-industry stafng patterns (shares of occupations for each industry).

3. Calculating alternative rates as total transfers, minus growth or decline, divided by estimated occupational employment for a base period.

Information about the separations methodology is available at: https:// www.bls.gov/opub/mlr/2018/article/occupational-separations-a-new-method-for-projecting-workforce-needs.htm and information about the alternative methodology is available at: https://esd.wa.gov/ labormarketinfo/projections.








For a complete list of separations and alternative projected employment, see: https://esd.wa.gov/labormarketinfo/projections.

Figure 5-5 presents a comparison between separations and alternative methodologies. Average annual total openings are compared at the two-digit SOC level. Alternative openings are on average almost two and a half times larger than separations openings. Te alternative method increase makes sense since it measures openings not tracked by BLS. Te alternative method measures turnover within occupations, while the BLS method does not. Also, BLS labor force exits measure national exits, but do not track exits from states.

Te average ratio for alternative to separations is 2.40. A ratio above this average means that a worker is more likely to change jobs within a given occupation than to transfer to another occupation.

In Figure 5-5, the three largest alternative-to-separations ratios are for construction and extraction (3.54), healthcare practitioners and technical (3.45) and management (2.99) occupations.




I I I I I I

Figure 5-5. Comparison of alternative and separations methodologies on total openings Washington state, 2018 and 2028 Source: Employment Security Department/LMEA; U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages

2-digit SOC Major occupational group

Est. empl. 2018

Est. empl. 2028

Alternative average annual total openings

2018-2028

Separations average annual total openings

2018-2028

Ratio alternative

to separations

11-0000 Management 226,457 273,377 89,582 29,964 2.99 13-0000 Business and financial operations 253,233 302,054 86,975 36,349 2.39 15-0000 Computer and mathematical 198,214 253,281 74,407 49,939 1.49 17-0000 Architecture and engineering 85,982 94,081 22,011 8,556 2.57 19-0000 Life, physical and social science 43,508 50,296 12,558 5,708 2.20 21-0000 Community and social service 60,901 76,556 21,853 10,632 2.06 23-0000 Legal 29,884 33,508 8,480 2,849 2.98 25-0000 Education, training and library 209,714 248,910 56,038 28,964 1.93 27-0000 Arts, design, entertain., sports and media 78,429 95,280 29,895 13,412 2.23 29-0000 Healthcare practitioners and tech. 180,624 215,182 61,091 17,723 3.45 31-0000 Healthcare support 153,568 184,843 64,508 25,645 2.52 33-0000 Protective service 67,891 77,363 20,866 10,075 2.07 35-0000 Food prep. and serving related 268,838 320,379 132,980 60,929 2.18 37-0000 Building and grounds cleaning and maint. 121,902 147,413 55,671 22,698 2.45 39-0000 Personal care and service 116,093 138,263 52,722 23,509 2.24 41-0000 Sales and related 357,349 381,641 128,594 54,947 2.34 43-0000 Office and administrative support 412,045 460,239 141,187 60,804 2.32 45-0000 Farming, fishing and forestry 100,477 102,023 47,331 16,478 2.87 47-0000 Construction and extraction 237,916 252,704 105,422 29,741 3.54 49-0000 Installation, maintenance and repair 150,151 162,651 51,372 17,710 2.90 51-0000 Production 205,170 212,710 59,732 25,503 2.34 53-0000 Transportation and material moving 267,171 290,902 100,887 41,269 2.44 00-0000 Totals 3,825,517 4,373,656 1,424,158 593,401 2.40

On average, alternative openings are 2.40 times larger than separations openings.



Specific occupations

Figure 5-6 shows the top 20 specifc occupations by total openings based on the separations methodology. Figure 5-7 shows the top 20 specifc occupations by total openings based on the alternative methodology.

Te number of openings due to job growth did not exceed openings due to separations or alternative job turnover in any of the top 20 occupations.

For the separations methodology, the sofware developers occupation is projected to have the largest number of total openings, while for the alternative methodology, retail salespersons occupations are projected to have the largest number of total openings. Sixteen of the top 20 specifc occupations are the same occupations in both methods.

Figure 5-6. Top 20 specific occupations by average annual total openings, separations methodology Washington state, 2018 to 2028 Source: Employment Security Department/LMEA; U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages, Occupational Employment Statistics

■

■

I I I I

Occu

patio

ns

Software developers Retail salespersons

Waiters and waitresses Cashiers

Combined food prep. and serving workers, incl. fast food Office clerks, general

Home health and personal care aides Laborers and freight, stock and material movers, hand

Janitors and cleaners, exc. maids and housekeeping cleaners Customer service representatives

Construction laborers Teaching assistants, all other

Cooks, restaurant General and operations managers

Secretaries and admin. assists., exc. legal, medical and executive Business operations specialists, all other

Registered nurses Landscaping and groundskeeping workers

Management analysts First-line supervisors of food preparation and serving workers

0 5,500 11,000 16,500 22,000 27,500 33,000

Average annual openings due to growth

Average annual openings due to separations

--In the separations methodology, the number of openings due to job growth did not exceed openings due to job turnover in any occupations.



Figure 5-7. Top 20 specific occupations by average annual total openings, alternative methodology Washington state, 2018 to 2028 Source: Employment Security Department/LMEA; U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages, Occupational Employment Statistics

■

■

I I I I

Occu

patio

ns

Retail salespersons Software developers

Office clerks, general Waiters and waitresses

Home health and personal care aides Combined food prep. and serving workers, incl. fast food

Laborers and freight, stock and material movers, hand Construction laborers

Cashiers Janitors and cleaners, exc. maids and housekeeping cleaners

Carpenters General and operations managers Customer service representatives

Registered nurses Cooks, restaurant

Business operations specialists, all other Nursing assistants

Heavy and tractor-trailer truck drivers Maintenance and repair workers, general

Landscaping and groundskeeping workers

0 5,500 11,000 16,500 22,000 27,500 33,000 38,500

Average annual openings due to growth Average annual openings due to alternative rate

In the alternative methodology, the number of openings due to job growth did not exceed openings due to job turnover in any occupations.


Chapter 6: Income On the surface, wage and income data over the past several years seem to tell a story of rising aggregate prosperity. Median household income and wages continue to increase steadily. However, inequality has increased, and other indicators of economic distress have been rising too. Tis chapter explores some other stories of income and well-being in Washington state as told through other data sources.

Tis chapter documents Washingtonians’ income prior to the COVID-19 global pandemic. Te data referenced in this section reference data from the U.S. Census Bureau, and are up to date as of 2019, the fnal year of the longest economic expansion in recent history.8 Census snapshots for 2020 will be published in late 2021.

Household9 and family income Te Great Recession was characterized by deep employment losses from 2008 to 2010. Employment in Washington state began to recover in 2010, and continued to expand until early 2020, with some variation by industry and geography. Employment tallies tell important stories about industrial change and regional transformation, but whether increasing employment results in improvements in quality of life requires further investigation. It is important that we assess how we value work itself.. Tis chapter explores measures related to household income and well-being for Washington residents.

Tis chapter describes trends in household income, as published by the U.S. Census Bureau’s American Community Survey (ACS). When reading ACS reports, it is important to consider the following:

1. Income is not limited to earnings from wages. Household income, as defned by the Census Bureau, is derived from fve sources: earnings from wages, earnings from self-employment, investment income, transfer payments such as Social Security, and private retirement payments.

2. Each annual observation represents a statistical snapshot of a place in a moment of time. Language about increasing income means that the annual income of a region increased, but does not address the mechanisms underlying that change. Tat is, rising income could refect year-to-year pay raises; it could also refect wealthy neighbors moving into the neighborhood.

8 All data and analyses presented chapter are based on the U.S. Census Bureau American Community Survey (ACS) and have been adjusted for inflation to 2019 dollars. Data from previous annual reports will differ from figures for corresponding years in this report because of that adjustment.

9 The U.S. Census Bureau divides households into two types. A family household contains at least two people, and at least one other person in the household is related to the householder by birth, marriage or adoption. A non-family household may contain only one person or additional people that are not related to the householder.


Chapter 6 Income and wages

I I I I I I -------

In step with widespread employment losses, household incomes fell during the Great Recession. Unlike employment, which bottomed out in 2010 and subsequently climbed to pre-recession peak levels by 2013, income recovery took longer to materialize (Figure 6-1). According to the U.S. Census Bureau, the real10 median household income in Washington state declined by $4,535 or 6.6 percent from 2008 to 2010 and remained fat until 2013.

Te median didn’t begin to increase until 2014, but has increased every year since then. Looking at the past fve years (2015 to 2019), the median household income for Washington households increased by a total of $9,969 or 14.5 percent.

Te median Washington household income increased more quickly than the median national household income, which grew by $5,945 or 9.9 percent over the same time period. While a number of diferent explanations contribute to this fnding, it is worth pointing out that Washington added about 445,000 new residents11 over that time period, and that some of the highest employment growth rates have been observed in high-wage industries including information, professional and business services, and online retail trade.

Te median income for family households12 increased by $12,248 or 14.9 percent, while the median income for non-family households13 increased by $6,974 or 16.2 percent.

Figure 6-1. Median household income in 2019 dollars United States and Washington state, 2015 through 2019 Source: U.S. Census Bureau, American Community Survey

Household type 2015 2016 2017 2018 2019 Change, 2015 to 2019 All households, U.S. $59,766 $60,752 $62,585 $62,990 $65,712 9.9% All households, Washington $68,718 $70,757 $73,625 $75,332 $78,687 14.5%

Family households $82,461 $85,654 $87,747 $89,142 $94,709 14.9% Non-family households $43,070 $43,772 $45,861 $47,587 $50,045 16.2%

Real median household income increased by 14.5 percent in Washington state from 2015 to 2019.

10 Adjusted for inflation using the PCE deflator. 11 According tto the U.S. Census Bureau, American Community Survey, Washington’s population in

2015 was 7,163,657 and the population in 2019 was 7,614,893. 12 According to the U.S. Census Bureau, “A family includes a householder and one or more people

living in the same household who are related to the householder by birth, marriage, or adoption. All people in a household who are related to the householder are regarded as members of his or her family.”

13 A nonfamily household consists of a householder living alone (a one-person household) or where the householder shares the home only with people to whom he/she is not related (e.g., a roommate).


Income and wages Chapter 6

Te following section describes a selection of income-related statistics pertaining to Washington households according to the ACS. Supporting data are found in Figure 6-2.

Poverty During the Great Recession, the poverty rate14 for Washington individuals increased from 11.3 percent in 2008 to 14.1 percent in 2014. As of 2019, the poverty rate had fallen to 9.8 percent.

Children tend to have higher poverty rates than the general population. In 2019, 12.0 percent of children residing in Washington were living beneath the poverty threshold. Te peak rate of childhood poverty over the past decade was 18.8 percent in 2013.

Household earnings Te Great Recession had lasting efects on the share of households reporting earnings from wage employment. Te share of households reporting earnings from wage employment dropped from 81.3 percent in 2008 to 78.5 percent in 2013. Te portion of households reporting wage income in 2019 remained relatively low at 78.8 percent. Te large-scale episode of job loss experienced during the pandemic-induced recession will likely reveal a large downward shif in this measure in 2020.

Despite the relative drop in the portion of households reporting earnings from a job, average household earnings have increased steadily over time. As of 2019, the average household earnings from a job was $107,023, a statistically signifcant increase of 3.7 percent over the previous year.

Households can, and ofen do, include multiple wage earners that contribute income. It is also worth pointing out that average household earnings from a job actually exceed the median household income. While the median indicates the midpoint of statistical values, average household income can be infuenced by high-wage households that tug on the measurement.

Full time/part-time work For the most part, responses to the ACS are consistent in that the share of workers reporting full-time employment exceeds the share of workers reporting part-time employment. Of course, the opportunities to work decrease during recessions. Prior to the Great Recession, 61.6 percent of workers reported working full time (more than 35 hours per week).

14 Following the Office of Management and Budget’s (OMB’s) Directive 14, The Census Bureau uses a set of income thresholds that vary by family size and composition to determine who is in poverty. If the total income for a family falls below the relevant poverty threshold, then the family (and every individual in it) is considered in poverty.



By 2011, this proportion had decreased to 55.7 percent. As of 2019, the portion of workers reporting full-time employment returned to pre-recession levels.

Individuals reporting part-time work (fewer than 35 hours per week) increased from 19 percent in 2007 to 19.9 percent in 2011, as full-time work became less available during the recession. As the economy recovered, many jobs shifed from part time to fulltime hours. By 2019, the portion of workers reporting part-time employment was down to 17.9.

Earnings from a job or self-employment Infation-adjusted median earnings have increased every year since 2015. From 2018 to 2019, median earnings increased from $40,559 to $41,735, an increase of $1,176 or 3 percent. Te increase in earnings for full-time/ year-round workers increased by $3,344 or 6 percent over the year, raising the median to $59,526.

Comparing median earnings for male versus female full-time/year-round workers reveals a persistent earnings gap. Women’s median earnings ($50,612 in 2019) are 79 percent of men’s ($63,988). From 2018 to 2019, the median earnings for both female and male full-time workers increased. Women’s median earnings increased by $1,327 (3 percent) while men’s median earnings increased by $858 (1 percent). Te earnings gap decreased by one percentage point. In order to achieve equality, women’s median earnings would need to increase an additional $12,907 or 25 percent.

Despite proliferation of employment-related apps such as ride sharing, the proportion of people reporting self-employment has remained statistically unchanged over the past several years. In 2019, 5.7 percent of workers reported that they were employed in their own non-incorporated business.

Income other than from earnings15

Income includes several components, one of which is earnings. Tis section describes trends in about transfer payments and retirement income.

Baby Boomers (a particularly large generation) are reaching retirement age. Te proportion of households reporting Social Security and pension payments has increased gradually over the past several years. (Tis is one contributing factor to the phenomenon of decreasing households reporting wage earnings in the previous section.) As of 2019, 29.7 percent

15 Income data referenced in this section are published by the U.S. Census Bureau and are based on a household survey.



of Washington households received Social Security payments, and 25.0 percent received private pension payments. For perspective, the comparable fgures for 2015 were 29.0 percent and 19.2 percent respectively.

Te average annual payout for households collecting private pensions in 2019 was $29,541, or an average monthly payment of $2,462. In 2015, the monthly amount was $2,239.

Supplemental Security Income (SSI) is a federal program that pays benefts to disabled adults and children who have limited income and resources, and to people 65 years and older without disabilities who meet fnancial requirements. In 2019, 4.5 percent of all Washington households received SSI during the year. Tis proportion has decreased slightly each year since 2015. Te average monthly payment for households receiving SSI was $812 in 2019.

Te proportion of households collecting welfare cash payments increased rapidly during the Great Recession, reaching a peak of 4.6 percent in 2010. Te proportion of households receiving welfare has decreased almost every year since 2010. In 2019, 2.5 percent of Washington households received welfare cash payments. Te average monthly payout for welfare recipients was $256, up from $228 per month in 2018, but down from $382 (adjusted) in 2010.

Te Supplemental Nutrition Assistance Payment (SNAP) is a type of non-cash transfer payment for households that fall beneath certain income thresholds. As of 2019, 10.6 percent of Washington households received SNAP payments, commonly referred to as food stamps. Over the past 10 years, SNAP benefts have represented a portion of household income for at least 10 percent of households. In 2012, 15.1 percent of households received food stamps. Te proportion has decreased each year in the meantime.

Health insurance16

Prior to the introduction of the Afordable Care Act (ACA, commonly called “Obamacare”), the portion of Washington residents reporting no health coverage hovered around 14 percent. In 2014, the proportion of uninsured households dropped from 14 percent to 9.2 percent. By 2016, the proportion had dropped to 6 percent, and has remained low but has begun to increase slightly. As of 2019, 6.6 percent of Washington residents (496,047 individuals) reported that they had no health coverage.

For the most part, Washington residents with health insurance are covered in the private market – usually through their employers. Te proportion of households reporting private coverage has remained steady

16 Health insurance coverage data referenced in this section are published by the U.S. Census Bureau, and are based on a household survey.



since this question has been asked of respondents. During the great recession, responses dropped from 71.0 percent in 2008 to 68.5 percent in 2012. As of 2019, 71.1 percent of Washington residents were covered by private insurance. On the fip side, residents relying solely on public health insurance jumped from 17.2 percent in 2013 to 20.1 percent in 2014. In 2019, 20.1 percent of Washington residents relied solely on the public market for health insurance.

Homeownership and rent Te homeownership rate in Washington state decreased from 66.1 percent in 2007 to 61.7 percent in 2014. Since 2014, the rate has increased slightly every year. As of 2019, the rate was 63.1 – still well below observed rates prior to the Great Recession.

Te cost of living can vary substantially from one place to another, making income levels an inadequate measure when trying to assess local conditions. For example, the same level of household income can imply very diferent standards of living depending on whether you are residing in Bellevue or Yakima. One way to measure economic stress, regardless of geographic variation, is to compare the cost of housing relative to household income. Tirty percent is a common threshold for indicating economic duress, as there is a general recommendation that households spend less than 1/3 of their income on housing costs if possible.

Te percent of Washington households in economic distress due to high housing costs rose in 2008 and 2009, but then declined through the foreclosure process as a large number of homeowners transitioned to renters. Te percentage of renters exceeding that threshold increased during the recession, reaching 48.4 percent in 2010. By 2017, the proportion of economically distressed renters decreased to 45.2 percent. In 2018, the downward trend reversed. In 2018 and 2019, 47.7 percent of renters were reported to have paid more than 30 percent of household income on housing-related costs.

Homeowners with a mortgage paying more than 30 percent of their income toward housing rose in the lead-up to the recession, exceeding 40 percent from 2007 to 2010. Over the course of the recovery, that proportion has decreased, in part due to an overall decline of homeownership. By 2019, the proportion of economically distressed homeowners with a mortgage was 28.8 percent, well below pre-recession levels. On the surface, this appears to be a positive statistic. Note, however, that the data represent snapshots in time. Many economically distressed households of the past are now represented among renters.



I I I I

Figure 6-2. Selected household statistics Washington state, 2015 through 2019 Source: U.S. Census Bureau, American Community Survey

Household statistic 2015 2016 2017 2018 2019 Median household income $69,206 $71,455 $73,798 $75,231 $78,687 Median family income $82,874 $86,477 $88,239 $89,318 $94,709 Poverty rate, all individuals 12.2% 11.3% 11.0% 10.3% 9.8% Poverty rate, children under 18 15.5% 13.7% 14.3% 12.5% 12.0% Households with earnings from a job1 78.5% 78.8% 79.1% 79.0% 78.8% Average household earnings from a job2 $93,849 $97,931 $100,035 $103,206 $107,023 Full-time workers, percent of population aged 16-643 58.2% 59.5% 60.5% 63.9% 61.9% Part-time workers, percent of population aged 16-64 18.8% 18.7% 18.5% 18.5% 17.9% Median earnings for all workers $37,950 $38,682 $40,017 $40,559 $41,735 Median earnings for full-time, year-round workers $52,897 $52,839 $55,088 $56,182 $59,526 Median earnings for male full-time, year-round workers $63,989 $65,777 $65,785 $63,130 $63,988 Median earnings for female full-time, year-round workers $47,740 $47,618 $49,806 $49,285 $50,612 Percent of workers who are self-employed 5.9% 5.8% 5.7% 6.0% 5.7% Households receiving Social Security 29.0% 29.6% 29.4% 29.7% 29.7% Households receiving private pension payments 19.2% 19.7% 19.1% 19.6% 25.0% Avg. mo. payout for households receiving private pensions $2,239 $2,371 $2,360 $2,403 $2,462 Households receiving Supplemental Security Income (SSI)1 4.9% 4.8% 4.8% 4.6% 4.5% Average monthly payout for those receiving SSI $880 $887 $875 $884 $812 Households receiving welfare cash payments)1 3.5% 3.1% 3.0% 2.9% 2.5% Average monthly payout for welfare recipients $248 $245 $212 $228 $256 Households receiving food stamps)1 13.4% 12.6% 12.3% 11.1% 10.6% Residents without health insurance 6.6% 6.0% 6.1% 6.4% 6.6% Number of residents without health insurance 467,967 428,092 446,106 477,284 496,047 Residents with private health insurance 71.1% 71.4% 70.8% 70.6% 71.1% Residents relying solely on public health insurance 19.9% 20.3% 20.7% 20.7% 20.1% Renters paying more than 30 percent of income for housing 45.4% 44.9% 45.2% 47.7% 47.7% Homeownership rate 62.4% 62.5% 62.8% 62.8% 63.1% Homeowners with a mortgage paying more than 30 percent of income for housing 29.5% 29.2% 28.8% 29.1% 28.8%

1 Households may fall into more than one of these categories. 2 Includes earnings from all members in the household. 3 Full-time workers usually worked at least 35 hours per week (but may not be year-round workers).

In 2019, a number of indicators about the well-being of households in Washington showed continued improvement.



Income inequality Many of the metrics presented in the chapter describe a long economic recovery that continued through 2019. One of the challenges of working with aggregated data, however, is that nuanced stories and trends can get lost beneath the surface. Figure 6-3 illustrates the share of households that fell within certain income ranges in 2019 dollars. Examining household income ranges allows for a more nuanced view of how the economic recovery has varied across socioeconomic groups.

Over the past fve years, the proportion of households with $35,000 or less in annual income has steadily decreased. Households with income ranges less than $35,000 accounted for about 24 percent of all households in 2015. By 2019, the share was closer to 21 percent. Tis suggests poverty rates fell during the recovery.

Te share of middle income households declined slightly at the lower end and remained steady at the upper end. Overall, this is the household income range that experienced the least change proportionally. From 2015 to 2019, the share of households with incomes between $35,000 and $100,000 per year barely changed, decreasing from about 43 percent in 2015 to just under 41 percent in 2019.

Meanwhile, upper and upper-middle income households increased as a share of total Washington households over the past fve years. Households earning more than $100,000 per year increased as a share of total households each year from 2015 through 2019. Over that time period, the share of households with incomes exceeding $100,000 per year expanded from about 32 percent in 2015 to 39 percent in 2019.



rjj

20%

18%

16%

Ji! 14% 0

&: 3; 12% = j 10%

--0 8% ~ ~ 41 6% ~

t 4%

2%

0%

■ 201 5 ■ 201 6 2017 ■ 2018 1 2019

..... -

- ..... -

- - - - -,_ - - - ..... - - -

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till - - - - - - -

<:10K 10K- ·:15K- $25K- $35K- : K- $75K- :100K- :1wK- :200K ' 15K 25K $35K K $75K 100K : 150K 200K

Hou:s,ehollcl income range in 2019 cl ol lars

Figure 6-3. Percent of households by income range, 2019 dollars Washington state, 2015 through 2019 Source: U.S. Census Bureau, American Community Survey

The share of households in upper income brackets continued to rise in 2019, while the proportion of lower income households decreased proportionally.


Chapter 7: Economic comparisons with other states Tis chapter presents several tables of economic data, comparing Washington to the nation as a whole as well as other states and the District of Columbia. Minimum wage, unemployment rate, job growth, annual exports, per capita income, privately owned building permits and median single-family home costs are presented as economic indicators for comparison as well as a current ranking for Washington state.

• Figure 7-1 shows the growth of the minimum wage in Washington state compared to other states. Currently, Washington state has the second highest minimum wage of $13.50 per hour with only the District of Columbia with a higher rate of $15.00.

• Figure 7-2 depicts the unemployment rate for Washington compared to other states and the nation. In 2019, Washington state was in 42nd place.

• Figure 7-3 shows the average annual job growth rate of each state. As of 2019, Washington state had an average annual job growth rate of 1.24 percent, placing eighth in the nation.

• Figure 7-4 ranks annual exports for each state. In 2019, Washington was in ffh place with over $60 billion in annual exports. Tese fgures are specifcally tied to the exports fowing through ports and terminals, and only refect the value of goods fowing through Washington state, which are not necessarily produced within the state.

• Figure 7-5 compares per capita income and average annual growth rate by state for 2009 and 2019. Washington ranks seventh for income and second for growth.

• Figure 7-6 shows the number of building permits for 2009 and 2019. Washington ranked seventh in 2009 and sixth in 2019.

• Figure 7-7 shows median single-family house prices in metropolitan statistical areas (MSAs) as well as the rate of change between 2017 and 2019. Several MSAs in Washington are included in this list with the Seattle-Tacoma-Bellevue area listed as the eighth highest with a median house price of $524,700 and a 12.6 percent rate of change between 2017 and 2019. Te Kennewick-Richland MSA, Spokane-Spokane Valley MSA and Yakima MSA were in 36th, 54th, and 63rd place respectively.


Chapter 7 Economic comparisons with other states

I I

'

' -

-

'

'

'

' -

Minimum wage

Figure 7-1 States with minimum wage higher than federal minimum wage, based on 2019 ranking United States and Washington state, 2010, 2015 and 2020 Source: U.S. Department of Labor

Rank State 2010 2015 2020 United States $7.25 $7.25 $7.25

1 District of Columbia $8.25 $10.50 $15.00 2 Washington $8.55 $9.47 $13.50 3 Massachusetts $8.00 $9.00 $12.75 4 Arizona $7.25 $8.05 $12.00 4 California $8.00 $9.00 $12.00 4 Colorado $7.24 $8.23 $12.00 4 Connecticut $8.25 $9.15 $12.00 4 Maine $7.50 $7.50 $12.00 4 Oregon $8.40 $9.25 $12.00 5 New York $7.25 $8.75 $11.80 6 Maryland $7.25 $8.25 $11.00 6 New Jersey $7.25 $8.38 $11.00 7 Vermont $8.06 $9.15 $10.96 8 Rhode Island $7.40 $9.00 $10.50 9 Alaska $7.75 $8.75 $10.19 10 Hawaii $7.25 $7.75 $10.10 11 Arkansas $6.25 $7.50 $10.00 11 Illinois $8.25 $8.25 $10.00 11 Minnesota $6.15 $9.00 $10.00 12 Michigan $7.40 $8.15 $9.65 13 Missouri $7.25 $7.65 $9.45 14 South Dakota $7.25 $8.50 $9.30 15 Delaware $7.25 $8.25 $9.25 16 Nebraska $7.25 $8.00 $9.00 16 Nevada $7.55 $8.25 $9.00 16 New Mexico $7.50 $7.50 $9.00 17 West Virginia $7.25 $8.00 $8.75 18 Ohio $7.30 $8.10 $8.70 19 Montana $7.25 $8.05 $8.65 20 Florida $7.25 $8.05 $8.56


Economic comparisons with other states Chapter 7

I I I I

Unemployment rates

Figure 7-2. Highest and lowest state unemployment rates, not seasonally adjusted, based on 2019 ranking United States and Washington state, 2009, 2014 and 2019 Source: U.S. Bureau of Labor Statistics, Local Area Unemployment Statistics

Rank State 2009 2014 2019 United States 9.3% 6.2% 3.7%

1 North Dakota 4.1% 2.7% 2.4% 1 Vermont 6.6% 3.9% 2.4% 3 New Hampshire 6.2% 4.3% 2.5% 4 Utah 7.3% 3.8% 2.6% 5 Hawaii 7.2% 4.4% 2.7% 5 Iowa 6.4% 4.2% 2.7% 7 Colorado 7.3% 5.0% 2.8% 7 South Carolina 11.2% 6.5% 2.8% 7 Virginia 6.7% 5.2% 2.8% 10 Idaho 8.8% 4.8% 2.9% 10 Massachusetts 8.1% 5.7% 2.9% 37 California 11.2% 7.5% 4.0% 37 Illinois 10.2% 7.1% 4.0% 37 New York 8.3% 6.3% 4.0% 40 Michigan 13.7% 7.2% 4.1% 40 Ohio 10.3% 5.8% 4.1% 42 Kentucky 10.3% 6.5% 4.3% 42 Washington 9.2% 6.1% 4.3% 44 Pennsylvania 8.0% 5.9% 4.4% 45 Arizona 9.9% 6.8% 4.7% 46 Louisiana 6.8% 6.4% 4.8% 47 New Mexico 7.5% 6.7% 4.9% 47 West Virginia 7.7% 6.6% 4.9% 49 Mississippi 9.5% 7.5% 5.4% 50 District of Columbia 9.3% 7.8% 5.5% 51 Alaska 7.7% 6.9% 6.1%



Nonfarm employment

Figure 7-3. Highest and lowest state average annual job growth rates, nonfarm employment United States and Washington state, 2000 to 2019 Source: U.S. Bureau of Labor Statistics, Current Employment Statistics

Rank State Average annual growth rate United States 0.72%

1 Utah 1.98% 2 Nevada 1.71% 3 Idaho 1.62% 4 Texas 1.60% 5 North Dakota 1.55% 6 Arizona 1.43% 7 Florida 1.26% 8 Washington 1.24% 9 Colorado 1.22% 10 Montana 1.12% 11 Dist. of Columbia 1.09% 12 Wyoming 1.01% 40 Missouri 0.28% 41 Indiana 0.28% 42 Maine 0.27% 43 Wisconsin 0.27% 44 New Jersey 0.26% 45 Louisiana 0.19% 46 Illinois 0.07% 47 Mississippi 0.02% 48 Connecticut -0.02% 49 Ohio -0.04% 50 West Virginia -0.12% 51 Michigan -0.28%



Annual exports

Figure 7-4. Highest and lowest state annual exports,* based on 2019 ranking United States and Washington state, 2009, 2014 and 2019 Source: U.S. Department of Commerce, Office of Trade and Economic Analysis

Rank State 2009 2014 2019 United States $1,056,042,963,028 $1,621,873,792,910 $1,643,160,852,937

1 Texas $162,994,740,450 $285,559,318,623 $328,863,813,668 2 California $120,079,965,765 $173,868,587,862 $174,026,007,377 3 New York $58,743,030,056 $88,834,326,287 $75,653,310,649 4 Louisiana $32,616,451,452 $64,770,099,653 $63,699,991,096 5 Washington $51,850,856,743 $90,558,268,785 $60,309,651,239 6 Illinois $41,626,110,699 $68,394,004,251 $59,723,534,413 7 Florida $46,888,006,761 $58,438,831,859 $55,995,357,602 8 Michigan $32,655,333,884 $57,573,110,364 $55,802,054,729 9 Ohio $34,104,484,238 $52,641,380,990 $53,229,254,204 10 Pennsylvania $28,381,102,168 $40,410,834,695 $42,722,413,031 42 Delaware $4,311,773,339 $5,267,417,899 $4,407,150,210 43 District of Columbia $1,090,543,044 $940,230,516 $3,690,001,612 44 Idaho $3,877,389,493 $5,137,755,733 $3,433,920,401 45 Vermont $3,219,270,656 $3,669,605,649 $3,021,350,506 46 Maine $2,231,142,502 $2,811,060,491 $2,723,661,201 47 Rhode Island $1,495,522,447 $2,388,479,292 $2,675,361,663 48 Montana $1,053,312,395 $1,544,908,682 $1,684,788,208 49 Wyoming $926,141,589 $1,757,262,877 $1,366,651,503 50 South Dakota $1,010,960,601 $1,577,588,645 $1,357,038,063 51 Hawaii $563,059,688 $1,447,489,746 $453,799,650

*Annual exports represent the value of goods flowing through ports/terminals. These goods may originate from places other than the port-state and thus export values do not necessarily reflect the health of the economy in the state where the port(s) are located.



Personal income

Figure 7-5. Highest and lowest state per capita personal income,1 in 2019 dollars,2 based on 2019 ranking United States and Washington state, 2009 and 2019 Source: U.S. Bureau of Economic Analysis

Rank State 2009 2019 Average annual

growth rate3

United States $39,376 $56,490 3.7% 1 District of Columbia $59,998 $83,406 3.3% 2 Connecticut $60,428 $77,289 2.5% 3 Massachusetts $51,412 $74,187 3.7% 4 New York $46,916 $71,717 4.3% 5 New Jersey $50,567 $70,471 3.4% 6 California $42,224 $66,619 4.7% 7 Washington $41,844 $64,758 4.46% 8 Maryland $48,845 $64,640 2.8% 9 New Hampshire $45,742 $63,502 3.3% 10 Alaska $46,834 $62,806 3.0% 42 Oklahoma $34,920 $47,341 3.1% 43 Arizona $33,746 $46,058 3.2% 44 Idaho $31,436 $45,968 3.9% 45 South Carolina $31,635 $45,438 3.7% 46 Arkansas $31,372 $44,629 3.6% 47 Alabama $32,685 $44,145 3.1% 48 Kentucky $32,304 $43,770 3.1% 49 New Mexico $32,523 $43,326 2.9% 50 West Virginia $31,412 $42,315 3.0% 51 Mississippi $29,801 $38,914 2.7%

1 Per capita personal income is total personal income divided by total mid-year population. 2 Note – All dollar estimates are millions of current dollars (not adjusted for inflation). Calculations are performed on unrounded data.

3 Last updated: September 24, 2020 -- revised statistics for 2013 to 2019.



Building permits

Figure 7-6. Highest and lowest states in number of authorized privately owned building permits, based on 2019 ranking United States and Washington state, 2009 and 2019 Source: U.S. Census Bureau

Rank State 2009

building permits 2019

building permits Percent change

2009 to 2019 United States 582,963 1,386,048 137.8%

1 Texas 84,440 209,895 148.6% 2 Florida 35,329 154,302 336.8% 3 California 35,069 110,197 214.2% 4 North Carolina 33,800 71,307 111.0% 5 Georgia 18,228 53,823 195.3% 6 Washington 17,011 48,424 184.66% 7 Arizona 14,474 46,580 221.8% 8 New York 18,344 45,219 146.5% 9 Tennessee 15,005 41,361 175.6% 10 Colorado 9,355 38,633 313.0% 41 Montana 1,686 4,776 183.3% 42 Maine 3,121 4,760 52.5% 43 New Hampshire 2,287 4,743 107.4% 44 South Dakota 3,691 4,415 19.6% 45 Hawaii 2,617 4,093 56.4% 46 West Virginia 2,235 3,010 34.7% 47 North Dakota 3,195 2,495 -21.9% 48 Vermont 1,367 1,801 31.7% 49 Wyoming 2,294 1,708 -25.5% 50 Alaska 916 1,680 83.4% 51 Rhode Island 961 1,400 45.7%



I I I I

Home prices

Figure 7-7. Median single-family house prices, based on 2019 ranking Selected U.S. metropolitan areas, 2017 and 2019 Source: National Association of Realtors

Rank Metropolitan area 2017 2019

Percent change

2017 to 2019 United States $248,800 $274,600 10.4%

1 San Jose-Sunnyvale-Santa Clara, CA $1,180,000 $1,265,000 7.2% 2 San Francisco-Oakland-Hayward, CA $900,000 $988,000 9.8% 3 Anaheim-Santa Ana-Irvine, CA $780,000 $825,000 5.8% 4 Urban Honolulu, HI $757,300 $802,500 6.0% 5 San Diego-Carlsbad, CA $599,000 $645,000 7.7% 6 Boulder, CO $566,100 $618,600 9.3% 7 Los Angeles-Long Beach-Glendale, CA $550,800 $611,200 11.0% 8 Seattle-Tacoma-Bellevue, WA $465,800 $524,700 12.6% 9 Boston-Cambridge-Newton, MA-NH $452,900 $491,900 8.6% 10 Nassau County-Suffolk County, NY $462,000 $491,600 6.4% 11 Denver-Aurora-Lakewood, CO $414,700 $462,100 11.4% 17 Portland-Vancouver-Hillsboro, OR-WA $381,800 $409,300 7.2% 29 Salem, OR $265,500 $310,700 17.0% 30 Eugene, OR $264,600 $308,600 16.6% 36 Kennewick-Richland, WA $243,600 $299,800 23.1% 54 Spokane-Spokane Valley, WA $223,400 $265,300 18.8% 63 Yakima, WA $204,100 $248,900 22.0% 173 Rockford, IL $117,800 $128,300 8.9% 174 Wichita Falls, TX $114,900 $125,900 9.6% 175 Erie, PA $115,700 $125,300 8.3% 176 Peoria, IL $122,600 $120,700 -1.5% 177 Binghamton, NY $109,600 $119,400 8.9% 178 Elmira, NY $110,400 $112,500 1.9% 179 Cumberland, MD-WV $90,700 $106,700 17.6% 180 Youngstown-Warren-Boardman, OH-PA $86,100 $102,600 19.2% 181 Decatur, IL $94,400 $96,500 2.2%


-

Appendices Appendix 1: Washington’s workforce development areas Appendix figure A1-1. Washington state workforce development aeas (WDAs)

11

08 12

10

03

04

05

06

02

01

07

09

San Juan

Clallum

Mason Grays Harbor

Jefferson

Island

Kitsap

Thurston

LewisPacific

Skamania

Franklin

Benton

Clark

Cowlitz Wahkiakum

Snohomish

Whatcom

Skagit

King

Klickitat

Yakima

Grant

Pierce

Spokane

Adams

Okanogan

Douglas Chelan

Kittitas

Walla Walla

Columbia

Garfield

Whitman

Pend Oreille

Asotin

Stevens

Ferry

Lincoln

I I -I I - I

I I - I I I

WDA 1 – Olympic Consortium WDA 2 – Pacific Mountain WDA 3 – Northwest Washington WDA 4 – Snohomish WDA 5 – Seattle-King WDA 6 – Tacoma-Pierce

WDA 7 – Southwest Washington WDA 8 – North Central Washington WDA 9 – South Central Washington WDA 10 – Eastern Washington WDA 11 – Benton-Franklin WDA 12 – Spokane -


Appendix 1 Workforce Development Areas


Appendix 2: Seasonal, structural and cyclical industry employment

Theoretical base for employment decomposition We used R’s advanced decomposition models for time series.

Decomposition of employment for each point in time (months, in our case) is:

Employment = (trend + cycle) + seasonal + irregular

Within the decomposed employment components, trends are a result of structural changes.

Tere are two steps in the process of time series decomposition:

1. We split the series between; combined trend (which includes trend + cycle), seasonal and irregular components.

2. We split the combined trend (trend + cycle) into trend and cyclical components.

Appendix fgure A2-1 represents the main components of decomposition for total nonfarm employment. Te trend component in the fgure is the result of the frst step of decomposition and represents the combination of trend plus cycle. Te trend plus cycle component is used in further processing steps later in the decomposition process.

Appendix figure A2-1. Total nonfarm employment time series and its main components Washington state, 2002 to 2019 Source: Employment Security Department/LMEA; U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages

tal covered employment 35l'!OOO()

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Appendix 2 Seasonal, structural and cyclical industry employment

T T-1 L (Yt - st)2 +A* L [ ((st+1 - st) - (st - st-1)]2 t=l t=2

We used a state space model with auto selection of model variations (types of error, trend and seasonality). Model variations can be additive, multiplicative, none, etc. Te sofware also includes the choice of 30 exponential smoothing variations. Te main advantage of this type of approach lies in the fact that the types of models are not predefned and thus can vary for diferent series. In standard U.S. Census Bureau ARIMA models, parameters are estimated for each series, but models are predefned and remain the same for all series.

Te sofware selects the model that minimizes the Akaike’s Information Criteria (AIC).

Te state space approach allows for the optimized selection of models for each individual series. Tis entails the selection of the best model and then parameters are subject to change as time periods change. Tis is a major diference from classical regression (one level models). In addition, under the new approach, regardless of the selection of seasonal or irregular models (additive or multiplicative), the sum of decomposition components (combined trend, seasonal and irregular) remains equal to initial series for each month.

In step two, we used the combined trend series from step one for our analyses of the contributions of structural and cyclical components to growth. To accomplish this, we used the Hodrick-Prescott (HP) flter. Tis flter is a smoothing method that is widely used among macroeconomists to obtain a smooth estimate of the long-term trend component of a series.

Technically, the HP flter is a two-sided linear flter that computes the smoothed series s of y by minimizing the variance of y around s, subject to a penalty that constrains the second diference of s. Tat is, the HP flter chooses s to minimize:

Te penalty parameter λ controls the smoothness of the series s. Te larger the λ, the smoother the s. As λ=∞, s approaches a linear trend.

Seasonal, structural and cyclical industry employment Appendix 2

T T-l

L (Yt - st) 2 + 14,400 * L [ ((st+i - st) - (st - st_ 1 )]2 t=l t=2

We used default value λ=14,400 for monthly frequencies. Tis default value was defned by dividing the number of months per year by four raised to a power (default value 2)17 and multiplying by 1,600. For our purpose, for all series regardless of the model selected, the HP flter chooses s to minimize:

Industry seasonality levels Te level of employment seasonality for an industry is defned as an average of absolute values of the seasonal component divided by the initial series (mean (|seasonal| /employment)). Te levels are presented in column three of Appendix fgure A2-2. A larger level value indicates a larger seasonality value for the industry. To interpret the seasonal factors, arbitrary thresholds were established. Industries with a seasonal factor value of up to 1.0 percent were identifed as not seasonal. Industries with a factor value greater than 1.0 and up to 2.0 percent were identifed as having low levels of seasonality. Industries with a factor value greater than 2.0 and up through 4.0 percent were identifed as having moderate levels of seasonality, while industries with a factor value greater than 4.0 percent were considered to have high levels of seasonality. Te results are listed in column four.

Structural and cyclical contributions to industry employment changes Relative contributions to monthly employment change are calculated as the average for all months of absolute diferences (one-month diference) for specifc factors (presented in columns fve and six of the table in Appendix fgure A2-2). Te percentages of relative contributions for trend (structural) and cycle components are presented in columns seven and eight. Te industry that had the lowest cyclical component contribution (8.6 percent) was ambulatory healthcare services, while support activities for mining had the highest cyclical component contribution (67.2 percent). Te structural component (trend) accounted for the dominant share of change in total employment (79.7 percent), while the cyclical component accounted for the residual (20.3 percent).

17 We stayed with the power of two for this analysis, but the other possibility is to use the power of four.




I I I I I I

Appendix figure A2-2. Employment decomposition components Washington state, 2002 to 2019 Source: Employment Security Department/LMEA; Bureau of Labor Statistics, Quarterly Census of Employment and Wages

NAICS Industry Seasonal

factor Level

of seasonality

Trend (average number)

Cycle (average number)

Trend (percent)

Cycle (percent)

000 Total covered employment 1.35% Low 4,651 1,182 79.73% 20.27% 111 Crop production 31.61% High 118 139 45.80% 54.20% 112 Animal production and aquaculture 2.31% Moderate 6 5 52.10% 47.90% 113 Forestry and logging 2.37% Moderate 16 8 67.27% 32.73% 114 Fishing, hunting and trapping 5.71% High 4 5 44.64% 55.36% 115 Support activities for agriculture and forestry 17.09% High 72 60 54.42% 45.58% 212 Mining (except oil and gas) 3.46% Moderate 10 6 62.92% 37.08% 213 Support activities for mining 11.85% High 1 3 32.81% 67.19% 221 Utilities 0.73% No seasonality 7 6 52.60% 47.40% 236 Construction of buildings 2.47% Moderate 236 88 72.97% 27.03% 237 Heavy and civil engineering construction 7.38% High 69 31 69.26% 30.74% 238 Specialty trade contractors 3.11% Moderate 574 190 75.17% 24.83% 311 Food manufacturing 4.40% High 36 16 69.43% 30.57% 312 Beverage and tobacco product manufacturing 5.08% High 34 8 81.33% 18.67% 313 Textile mills 1.24% Low 1 1 54.84% 45.16% 314 Textile product mills 1.03% Low 5 4 53.94% 46.06% 315 Apparel manufacturing 1.29% Low 6 7 44.69% 55.31% 316 Leather and allied product manufacturing 5.27% High 1 2 45.40% 54.60% 321 Wood product manufacturing 1.10% Low 49 29 62.55% 37.45% 322 Paper manufacturing 0.62% No seasonality 27 13 66.90% 33.10% 323 Printing and related support activities 0.76% No seasonality 21 12 62.80% 37.20% 324 Petroleum and coal products manufacturing 1.78% Low 5 6 45.33% 54.67% 325 Chemical manufacturing 0.57% No seasonality 16 11 59.94% 40.06% 326 Plastics and rubber products manufacturing 0.77% No seasonality 19 16 54.70% 45.30% 327 Nonmetallic mineral product manufacturing 2.03% Moderate 23 14 63.05% 36.95% 331 Primary metal manufacturing 0.69% No seasonality 19 17 52.09% 47.91% 332 Fabricated metal product manufacturing 0.82% No seasonality 44 34 55.95% 44.05% 333 Machinery manufacturing 0.81% No seasonality 39 40 49.49% 50.51% 334 Computer and electronic product manufacturing 0.33% No seasonality 37 33 53.12% 46.88%

335 Electrical equipment, appliance, and component manufacturing 0.54% No seasonality 10 8 54.97% 45.03%

336 Transportation equipment manufacturing 0.59% No seasonality 237 204 53.74% 46.26% 337 Furniture and related product manufacturing 1.04% Low 28 13 68.22% 31.78% 339 Miscellaneous manufacturing 0.80% No seasonality 13 11 53.30% 46.70% 423 Merchant wholesalers, durable goods 0.47% No seasonality 122 68 64.28% 35.72% 424 Merchant wholesalers, nondurable goods 1.36% Low 46 28 62.03% 37.97%


I I I I I I NAICS Industry Seasonal

factor Level

of seasonality



Trend (percent)

Cycle (percent)

425 Wholesale electronic markets and agents and brokers 1.00% Low 105 32 76.39% 23.61% 441 Motor vehicle and parts dealers 1.07% Low 77 42 64.72% 35.28% 442 Furniture and home furnishings stores 1.87% Low 21 18 53.66% 46.34% 443 Electronics and appliance stores 2.64% Moderate 22 26 45.62% 54.38% 444 Building material and garden equip. and supplies dealers 3.57% Moderate 67 27 71.63% 28.37% 445 Food and beverage stores 1.38% Low 47 45 50.89% 49.11% 446 Health and personal care stores 1.13% Low 16 11 58.67% 41.33% 447 Gasoline stations 1.63% Low 11 9 55.56% 44.44% 448 Clothing and clothing accessories stores 4.28% High 64 63 50.34% 49.66% 451 Sporting goods, hobby, musical instrument, and book stores 3.34% Moderate 24 18 56.62% 43.38% 452 General merchandise stores 3.08% Moderate 136 61 68.89% 31.11% 453 Miscellaneous store retailers 1.84% Low 47 16 74.71% 25.29% 454 Nonstore retailers 1.58% Low 250 92 73.09% 26.91% 481 Air transportation 0.62% No seasonality 43 19 69.94% 30.06% 483 Water transportation 3.19% Moderate 5 5 53.70% 46.30% 484 Truck transportation 2.12% Moderate 36 24 60.17% 39.83% 485 Transit and ground passenger transportation 2.24% Moderate 9 8 52.81% 47.19% 486 Pipeline transportation 1.53% Low 1 1 39.63% 60.37% 487 Scenic and sightseeing transportation 20.37% High 3 4 39.60% 60.40% 488 Support activities for transportation 1.03% Low 39 24 61.91% 38.09% 491 Postal service 3.96% Moderate 1 1 46.04% 53.96% 492 Couriers and messengers 5.30% High 37 22 62.84% 37.16% 493 Warehousing and storage 2.14% Moderate 51 46 52.45% 47.55% 511 Publishing industries (except internet) 0.99% No seasonality 138 50 73.27% 26.73% 512 Motion picture and sound recording industries 4.12% High 11 9 53.42% 46.58% 515 Broadcasting (except internet) 0.72% No seasonality 6 5 56.35% 43.65% 517 Telecommunications 0.41% No seasonality 49 29 62.30% 37.70% 518 Data processing, hosting, and related services 1.67% Low 56 33 63.10% 36.90% 519 Other information services 5.17% High 126 37 77.15% 22.85% 521 Monetary authorities-central bank 0.80% No seasonality 1 0 61.85% 38.15% 522 Credit intermediation and related activities 0.22% No seasonality 115 59 66.15% 33.85% 523 Securities, commodity contracts, and other financial

investments and related activities 0.36% No seasonality 19 15 55.28% 44.72%

524 Insurance carriers and related activities 0.34% No seasonality 38 27 58.24% 41.76% 525 Funds, trusts, and other financial vehicles 14.23% High 2 3 42.23% 57.77% 531 Real estate 1.27% Low 83 25 76.75% 23.25% 532 Rental and leasing services 2.96% Moderate 37 13 74.60% 25.40%

533 Lessors of nonfinancial intangible assets (except copyrighted works) 3.92% Moderate 4 3 60.77% 39.23%

541 Professional, scientific, and technical services 0.41% No seasonality 383 133 74.26% 25.74%




I I I I I I NAICS Industry Seasonal

factor Level

of seasonality



Trend (percent)

Cycle (percent)

551 Management of companies and enterprises 0.32% No seasonality 89 38 70.12% 29.88% 561 Administrative and support services 3.01% Moderate 393 172 69.62% 30.38% 562 Waste management and remediation services 0.84% No seasonality 29 30 49.12% 50.88% 611 Educational services 3.36% Moderate 92 19 82.58% 17.42% 621 Ambulatory healthcare services 0.29% No seasonality 287 27 91.41% 8.59% 622 Hospitals 0.32% No seasonality 148 55 72.78% 27.22% 623 Nursing and residential care facilities 0.28% No seasonality 54 30 63.87% 36.13% 624 Social assistance 1.44% Low 356 321 52.62% 47.38% 711 Performing arts, spectator sports, and related industries 10.56% High 17 12 58.67% 41.33% 712 Museums, historical sites, and similar institutions 3.83% Moderate 7 5 59.11% 40.89% 713 Amusement, gambling, and recreation industries 4.25% High 55 34 61.80% 38.20% 721 Accommodation 5.35% High 59 32 65.08% 34.92% 722 Food services and drinking places 1.95% Low 443 123 78.31% 21.69% 811 Repair and maintenance 1.00% Low 31 19 62.08% 37.92% 812 Personal and laundry services 0.98% No seasonality 57 16 78.06% 21.94% 813 Religious, grantmaking, civic, professional, and similar

organizations 2.25% Moderate 49 20 70.61% 29.39%

814 Private households 6.55% High 345 312 52.49% 47.51% 901 Federal government (other) 0.99% No seasonality 57 55 50.79% 49.21% 902 State government (other) 1.62% Low 73 59 55.64% 44.36% 903 Local government (other) 1.66% Low 335 70 82.71% 17.29%

Theoretical base to identify relations between industry and total employment Te Granger causality test is a technique for determining whether one time series is useful in forecasting another. Put another way: this test answers the question of whether a time series “X” causes time series “Y.” Also, it tests to see how much of the current “Y” values can be explained by past values of the same series, and then to see whether adding lagged values of “X” can improve the explanation.

In our case, the question is whether employment in specifc industries “Granger-causes” total employment.

Te results of Granger causality are not always clear enough to be able to state that a series “X” Granger-causes series “Y,” but not the other way around. In such cases, we can fnd that neither series Granger-causes the other, or that each Granger-causes the other.


Moreover, Granger causality does not imply true causality. If both series “X” and “Y” are driven by a common third process (variable, series), but with diferent lags, there would be Granger causality. However, the changes in one series would not have a signifcant efect on the other. To address this issue, we estimated Granger causality in both directions. We estimated specifc industry on total employment and total employment on specifc industry employment.

Results of industry and total employment analysis Te last fve columns of Appendix fgure A2-3 represent an attempt to connect employment time series for specifc industries with employment time series of total covered employment. Te frst of these fve columns represents correlations of series of monthly employment between industries and total employment, while the second of these columns represents correlations of the frst diferences (monthly changes) for the same series.

Te third of these fve columns represents an attempt to identify the industries for which monthly employment could help in predicting the next month’s total employment. F-statistics from the Granger causality test for time series, with a lag of one month, are presented in this column. Te value of “F” indicates the signifcance of the impact of employment in the industry on the next month’s total employment. Larger values indicate efects that were more signifcant. Probabilities for the rejection of the hypotheses of signifcance, associated with F-statistics, are listed in the next to last column. A lower probability indicates higher confdence that the efect is signifcant. To address the issue of possible mutual causality we also tested inverse causality of total employment on specifc industries. As previously noted, if both direct and inverse causality are signifcant, it means that an industry employment series might not be a good indicator for the next month’s total employment. Te last column of Appendix fgure A2-3 indicates if signifcant direct causality of industry on total employment without signifcant inverse causality exists (indicator “yes”). All other cases have an indicator of “no.” Te cutof for such defnitions was the following: p-value for direct test is not more than 0.01, but for inverse test not less than 0.1. Last year 23 industries have the indicator “yes.” Tis year 17 industries have an indicator of “yes.”

Te combination of predictive abilities (indicator “yes”) and correlation with total employment and total employment growth can be used to identify the main industries used as coincidental and leading (i.e., one-step-ahead) economic indicators. In addition, this combination can be used for the one-step-ahead prediction of employment changes. Te




I I I I I I

industries identifed by this process are beverage and tobacco product manufacturing; machinery manufacturing; motor vehicle and parts dealers; truck transportation and motion picture and sound recording industries.

Appendix figure A2-3. Relationships between industry and total employment Washington state, 2002 to 2019 Source: Employment Security Department/LMEA; Bureau of Labor Statistics, Quarterly Census of Employment and Wages (QCEW)

NAICS Industry

Coorelation with total

employment

Coorelation of first

differences

F-statistic Granger test

(one-month lag) Probability

Significant one-way impact

000 Total covered employment 100.0% 100.0% NA NA NA 111 Crop production 83.8% -20.4% 2.71 0.10 No 112 Animal production and aquaculture 92.1% 7.9% 35.17 0.00 No 113 Forestry and logging -73.7% 62.5% 17.19 0.00 No 114 Fishing, hunting and trapping -89.7% -3.0% 0.38 0.54 No 115 Support activities for agriculture and forestry 97.1% 19.5% 5.16 0.02 No 211 Oil and gas extraction 78.1% 5.8% 12.67 0.00 Yes 212 Mining (except oil and gas) -50.3% 55.6% 9.12 0.00 No 213 Support activities for mining 55.4% 27.7% 0.11 0.74 No 221 Utilities 66.0% 11.9% 161.81 0.00 No 236 Construction of buildings 60.7% 95.7% 0.25 0.61 No 237 Heavy and civil engineering construction 21.0% 81.8% 0.78 0.38 No 238 Specialty trade contractors 73.9% 95.7% 3.31 0.07 No 311 Food manufacturing 89.5% 48.1% 6.64 0.01 No 312 Beverage and tobacco product manufacturing 96.2% 54.1% 11.74 0.00 Yes 313 Textile mills -47.0% 48.4% 5.14 0.02 No 314 Textile product mills -23.7% 39.0% 44.87 0.00 No 315 Apparel manufacturing -58.4% 48.4% 9.04 0.00 Yes 316 Leather and allied product manufacturing -50.3% -27.7% 44.64 0.00 Yes 321 Wood product manufacturing -58.3% 69.2% 12.18 0.00 Yes 322 Paper manufacturing -80.7% 34.0% 1.13 0.29 No 323 Printing and related support activities -72.6% 75.6% 0.64 0.43 No 324 Petroleum and coal products manufacturing 65.6% 37.4% 0.01 0.91 No 325 Chemical manufacturing 95.4% 65.5% 0.00 0.96 No 326 Plastics and rubber products manufacturing -29.2% 73.2% 11.84 0.00 No 327 Nonmetallic mineral product manufacturing 37.0% 78.9% 0.34 0.56 No 331 Primary metal manufacturing -14.8% 49.7% 3.98 0.05 No 332 Fabricated metal product manufacturing 88.4% 83.7% 1.10 0.30 No 333 Machinery manufacturing 80.2% 74.0% 23.62 0.00 Yes 334 Computer and electronic product manufacturing -60.5% 71.1% 0.01 0.92 No


I I I I I NAICS Industry


employment


differences




335 Electrical equipment, appliance, and component manufacturing

91.1% 38.9% 121.32 0.00 No

336 Transportation equipment manufacturing 59.0% 33.0% 1.10 0.29 No 337 Furniture and related product manufacturing -36.2% 86.3% 2.16 0.14 No 339 Miscellaneous manufacturing 64.3% 61.7% 10.44 0.00 No 423 Merchant wholesalers, durable goods 54.7% 94.2% 4.91 0.03 No 424 Merchant wholesalers, nondurable goods 84.9% 84.1% 0.00 0.96 No

425 Wholesale electronic markets and agents and brokers

66.6% -40.2% 3.44 0.06 No

441 Motor vehicle and parts dealers 59.1% 78.9% 23.71 0.00 Yes 442 Furniture and home furnishings stores -33.8% 75.0% 8.84 0.00 Yes 443 Electronics and appliance stores 25.5% 43.2% 18.14 0.00 Yes

444 Building material and garden equipment and supplies dealers

86.7% 76.1% 4.95 0.03 No

445 Food and beverage stores 91.2% 39.4% 3.81 0.05 No 446 Health and personal care stores 90.4% 28.2% 10.43 0.00 No 447 Gasoline stations -67.3% 4.7% 11.26 0.00 Yes 448 Clothing and clothing accessories stores -24.9% 76.0% 1.03 0.31 No

451 Sporting goods, hobby, musical instrument, and book stores

-66.0% 66.1% 0.43 0.51 No

452 General merchandise stores 75.8% -43.0% 0.00 0.99 No 453 Miscellaneous store retailers 79.2% 71.5% 7.42 0.01 No 454 Nonstore retailers 95.7% 47.0% 12.35 0.00 No 481 Air transportation 63.6% 49.0% 3.16 0.08 No 482 Rail transportation -0.5% -52.7% 0.02 0.88 No 483 Water transportation 84.0% 34.2% 1.37 0.24 No 484 Truck transportation 63.3% 76.9% 10.30 0.00 Yes 485 Transit and ground passenger transportation 89.2% 25.5% 34.13 0.00 No 486 Pipeline transportation 85.2% 16.4% 46.24 0.00 No 487 Scenic and sightseeing transportation 75.8% 23.8% 47.75 0.00 No 488 Support activities for transportation 97.9% 65.1% 44.78 0.00 No 491 Postal service 54.6% 17.8% 9.68 0.00 No 492 Couriers and messengers 81.8% 61.1% 0.07 0.79 No 493 Warehousing and storage 92.5% 37.9% 9.82 0.00 No 511 Publishing industries (except internet) 95.1% 59.5% 87.83 0.00 No 512 Motion picture and sound recording industries 93.9% 52.2% 14.79 0.00 Yes 515 Broadcasting (except internet) -83.9% 55.5% 10.31 0.00 No 517 Telecommunications -88.7% 14.9% 46.20 0.00 Yes




I I I I I NAICS Industry


employment


differences




518 Data processing, hosting, and related services 93.0% 40.1% 7.42 0.01 No 519 Other information services 96.3% 45.1% 0.00 0.95 No 521 Monetary authorities-central bank -58.7% 73.7% 1.65 0.20 No 522 Credit intermediation and related activities -56.7% 42.6% 0.84 0.36 No

523 Securities, commodity contracts, and other financial investments and related activities

94.1% 63.2% 0.07 0.79 No

524 Insurance carriers and related activities 53.4% 56.7% 10.09 0.00 No 525 Funds, trusts, and other financial vehicles -83.2% 8.3% 33.91 0.00 No 531 Real estate 98.4% 74.7% 12.28 0.00 No 532 Rental and leasing services -53.1% 73.2% 4.46 0.04 No

533 Lessors of nonfinancial intangible assets (except copyrighted works)

-12.6% 72.8% 31.36 0.00 Yes

541 Professional, scientific, and technical services 96.5% 78.0% 7.72 0.01 No 551 Management of companies and enterprises 96.2% 62.1% 128.90 0.00 No 561 Administrative and support services 95.4% 80.4% 110.57 0.00 No 562 Waste management and remediation services 67.9% -3.6% 43.53 0.00 No 611 Educational services 95.6% 60.7% 0.21 0.65 No 621 Ambulatory health care services 95.2% 18.4% 2.08 0.15 No 622 Hospitals 92.7% -8.0% 3.10 0.08 No 623 Nursing and residential care facilities 81.9% -29.8% 1.04 0.31 No 624 Social assistance 93.7% 22.6% 11.88 0.00 Yes

711 Performing arts, spectator sports, and related industries

96.4% 37.2% 11.74 0.00 Yes

712 Museums, historical sites, and similar institutions 97.1% 45.5% 3.40 0.07 No 713 Amusement, gambling, and recreation industries 96.3% 69.3% 62.40 0.00 No 721 Accommodation 99.2% 85.2% 62.40 0.00 No 722 Food services and drinking places 99.7% 93.1% 114.66 0.00 No 811 Repair and maintenance 34.3% 86.8% 2.84 0.09 No 812 Personal and laundry services 97.1% 79.2% 1.09 0.30 No

813 Religious, grantmaking, civic, professional, and similar organizations

95.3% 32.4% 16.73 0.00 No

814 Private households -83.7% -43.3% 16.08 0.00 Yes 901 Federal government (other) 68.7% -54.3% 8.71 0.00 No 902 State government (other) 88.5% 14.2% 110.97 0.00 No 903 Local government (other) 97.0% 45.1% 104.76 0.00 No 901 Federal government (other) 45.8% -47.0% 7.07 0.01 No 902 State government (other) 87.9% 17.5% 113.98 0 No 903 Local government (other) 95.0% 2.5% 108.23 0 No

Significant, direct causality of industry on total employment, displays a “Yes” indicator in the last column.

Appendix 3: Use and misuse of employment projections Employment Projections are intended for career development over time, not as the basis for budget or revenue projections, or for immediate corrective actions within the labor market.

Employment projections provide a general outlook for industries and occupations in Washington state. Occupational projections show how many job openings are projected due to occupational employment growth and replacement needs (separations and alternative).18 For technical details see: 2019 Employment Projections Technical Report

For the separations method, replacement includes openings created by retirements and occupational separations. It does not measure turnover within occupations, i.e., when workers stay within the same occupation, but change employers. For the alternative method, replacement includes normal turnover as workers go from one employer to another while staying in the same occupation. Separations’ total openings from occupational projections do not represent total demand, but can be used as an indicator of demand. Alternative total openings for occupational projections do represent total demand. Total demand may be flled by new entrants to the state market. New entrants can be workers from other states or nations, and new entrants can also be graduates from this state, other states or nations. In addition, occupations can be flled by workers already within the market, within a given occupation or from another occupation. Available job openings cannot be reserved for any of these categories since the majority of jobs are open-competitive.

Occupational details for employment (with at least 10 jobs) are presented for the state and all workforce development areas in our employment projections data fles available online at https://esd.wa.gov/ labormarketinfo/projections.

Observed and predicted extremes in employment growth and other indicators, such as fastest-growing occupations and shortage of skills, can be used for placement and short-term training decisions. However, these should be limited for use when developing long-term education programs. Tere are two main reasons for this limitation:

18 This is discussed in the 2019 Employment Projections Technical Report at: https://esd.wa.gov/ labormarketinfo/projections. Due to the non-additive for calculating total openings, in this round of projections we calculated total openings for aggregated occupations as a total for detailed occupations. As a result, the aggregated level of total openings might not equal the total of growth plus replacement.







https://alternative).18


Appendix 3 Use and misuse of employment projections

1. First, with more education targeting occupations with skill shortages, there is a higher probability that this will cause an oversupply in those occupations and skill sets.19

2. Second, the general development of transferable skills is much more productive than trying to catch up with a skills shortage.

Te U.S. Bureau of Labor Statistics cautions on using Ofce of Management and Budget (OMB) classifcations: “Te 2018 SOC was designed solely for statistical purposes. Although it is likely that the 2018 SOC also will be used for various non-statistical purposes (e.g., for administrative, regulatory, or taxation functions), the requirements of government agencies or private users that choose to use the 2018 SOC for non-statistical purposes have played no role in its development, nor will OMB modify the classifcation to meet the requirements of any non-statistical program.

Consequently, the 2018 SOC is not to be used in any administrative, regulatory, or tax program unless the head of the agency administering that program has frst determined that the use of such occupational defnitions is appropriate to the implementation of the program’s objectives.”20

Diferent programs use diferent SOC coding systems. Combining the employment projections with other data sources generally requires a case-by-case analysis; an understanding of the diferences of each program should be clearly explained and properly handled.

19 Occupational projections are the basis of the Occupations in Demand list. This list is used for determining eligibility for a retraining program (Training Benefits), as well as other education and training programs. See: https://esd.wa.gov/labormarketinfo/LAAO

20 See: https://www.bls.gov/soc/2018/soc_2018_user_guide.pdf, page 24.

https://esd.wa.gov/labormarketinfo/LAAO

https://www.bls.gov/soc/2018/soc_2018_user_guide.pdf

Appendix 4: Occupations in Demand (OID) methodology Employment projections are the basis of the Occupations in Demand (OID) list covering Washington’s 12 workforce development areas and the state as a whole. Tis list is used to determine eligibility for a variety of training and support programs, but was initially created to support the unemployment insurance Training Benefts Program.

Te full OID list is accessible through the “Learn about an occupation” tool located at: https://esd.wa.gov/labormarketinfo/LAAO.

All occupations in the list have demand indication defnitions. Te defnitions come in three forms; in demand, not in demand or balanced. Tese defnitions indicate the probability of a job seeker gaining employment in a given occupation. Te term in demand indicates a greater probability of gaining employment. Te term not in demand indicates a lesser probability and balanced indicates an uncertain probability between success and failure in gaining employment.

Te defnitions are created through a four-step process.

The data sources for the OID list: Te 2020 list is based on projections with state specifc alternative rates used for turnover openings:

• Five-year projections for 2018 to 2023, using average annual growth rates and total job openings.

• Ten-year projections for 2018 to 2028, using average annual growth rates and total job openings.

• A combination of two-year (second quarter 2019 to second quarter 2021) and ten-year (2018 to 2028) projections, using average annual growth rates and total job openings.

All of these time frames use unsuppressed occupations with employment in a base year (2018), consisting of 50 or more employees, for the state and workforce development areas (WDAs).

In addition to projections, the OID list uses supply and demand data:

• Supply data: annual counts of unemployment claimants for WDAs for the period June 2019 to May 2020.

• Demand data: annual counts of job announcements from Te Conference Board, Help Wanted OnLine mid-monthly time series for the period June 2019 to May 2020.


https://esd.wa.gov/labormarketinfo/LAAO


Appendix 4 Occupations in Demand (OID) methodology

Step one: Identify initial “in demand” and “not in demand” categories for each period.

• For each time frame, occupations with average annual growth rates of at least 90 percent of their respective geographic area’s (statewide or WDA), total average annual growth rates and a share of total openings of at least 0.08 percent are defned as in demand.

• Occupations with average annual growth rates less than 70 percent of their respective geographic area’s total growth rates and a share of total openings of less than 1.0 percent are defned as not in demand.

Step two: Identify provisional occupational categories.

• If within any of the three projection time frames (fve-year, 10-year and two-/10-years combined), an occupation is categorized as being in demand, it receives the frst provisional identifcation as in demand.

• If within any of the three projection time frames, an occupation is categorized as not in demand, it receives a second provisional identifcation of not in demand.

Step three: Create final projections definitions.

• If an occupation has only one provisional defnition, it equals the fnal projections defnition.

• If an occupation has two provisional defnitions of in demand and not in demand, it gets identifed as balanced.

• All other occupations, without provisional defnitions (i.e., not meeting the thresholds from step one), are identifed as balanced.

Step four: Create final adjustment definitions.

Te projections defnitions are now put through an adjustment process, using current labor market supply/demand data which compares online job announcements to information on unemployment insurance (UI) claimants.

Adjustments are applied when current supply/demand data signifcantly contradicts the model-based projections defnitions.


Occupations in Demand (OID) methodology Appendix 4

The adjustment methodology • Supply/demand data are used for adjustments if they are signifcant.

Signifcant supply-demand data are those data where the share of the largest value between UI claimants and online job announcements are more than 1 percent of openings, and where the largest values between announcements and UI claimants more than 10, or the largest values between UI and announcements not less than fve, for the period 2017 to 2027.

• If the projections defnition is in demand or balanced but the ratio of supply to demand is more than 2.5, then the adjusted defnition is not in demand.

• If the projections defnition is in demand and the ratio of supply to demand is not larger than 2.5, but more than 1.5, then the adjusted defnition is balanced.

• If the projections defnition is not in demand or balanced, but the ratio of supply to demand is less than 0.4, then the adjusted defnition is in demand.

• If the projections defnition is not in demand and the ratio is at least 0.4, but less than 0.6, then the adjusted defnition is balanced.

The final list: Local adjustments Te Employment Security Department’s Labor Market and Economic Analysis division uses the methodology outlined above to prepare the initial lists for the state as a whole and by workforce development area. Tose lists are then given to local workforce development councils to review, adjust and approve based on their local experience and knowledge.

Appendix 4 Occupations in Demand (OID) methodology


Appendix 5: Skill projections In order to project skills, occupational projections are converted into skill projections. To project skills, we rely on the content of employers’ job postings rather than predefned, general O*NET skills.

Data sources Te main source for this analysis was a download of the top 100 hard skills for each detailed (six-digit SOC) occupation for Washington state from Te Conference Board, Help Wanted OnLine job announcements. Te downloaded fles represent extracted hard skills for each occupation from online job announcements, posted in the last three years (from July 2017 to June 2019).21 Only a maximum of 100 skills are available for each occupation. Each skill is displayed with the number of job announcements from which it was extracted. Te extracted skill numbers constitute a vector, up to a size of 100, for each occupation. A skill drawn from a greater number of job announcements is relatively more important. Te number of job announcements is summed for each occupation. Some occupations contain very few, if any listed skill components, and thus the summation value for a given occupation can be very small or nonexistent and are removed in later processes.

For creating skills-to-occupations matrices, we included occupations that satisfy the following conditions only:

1. Total skill counts are not less than fve.

2. Total skill counts are not less than 2.0 percent of base year employment.

3. Estimated employment for second quarter 2019 are not less than fve.

Each occupational vector of skill numbers was normalized (i.e., scaled) to totals of one.

By combining these vectors, we created skills-to-occupations matrices. Tese matrices were used to convert occupational estimations and projections into comparable numbers expressed as hard skills.

Te skills-to-occupations matrices are similar in structure and function to normalized matrices used for occupational/industries stafng patterns. Te skills-to-occupations matrices were based on statewide data and were used to convert occupational projections for the state and all WDAs into skills projections.

21 In last year’s projections report we used a sample for the period July 2015 to June 2018.


https://2019).21


Appendix 5 Skill projections

Afer conversion, we deleted all records where estimated or projected employment numbers were less than fve. We consider estimations below fve as unreliable. As a result of excluding missing skill/occupation vectors and removing results below fve, only a portion of the occupational employment estimates were converted into skills.

A uniform skill to occupation stafng matrix is applied to all areas. Due to diferences in occupational employment in each area, and the exclusion of employment below fve, available skill counts in each area vary. As a result, the largest number of detailed skills were 3,422 for Washington state, followed by the Seattle-King County WDA at 2,917. Te lowest number was for Eastern Washington at 1,218 skills.

Te conversion size of occupational employment to skills employment, calculated on base year employment (second quarter 2019), varies between 86.07 percent for the Eastern WDA to a low of 61.15 percent for the Washington state WDA. Te combined ratio for all WDAs is 77.91 percent and for the state is 61.15 percent.

Some results Te top six detailed hard skills for the state and all areas, based on projected numbers of total openings, for all time periods (second quarter 2019 to second quarter 2021, 2018 to 2023 and 2023 to 2028), with base year in second quarter 2019, are relatively stable between areas (order may vary). Te top six skills based on projected numbers of job openings for all time periods for the state are: Microsof Ofce, Food preparation, Bilingual, Quality Assurance, Microsof PowerPoint and Quality control. Te combined top six skills represent 16.89 percent of total openings for the state.

Last year in 2019, for the state and Seattle-King County, the top 32 skills with annual openings of at least 100, with the largest average annual growth rates from 2017 to 2027, for all periods, was projected for skills related to information technology (IT). Tis year however, for the period 2018 to 2028, other skill groups edged out IT as the sole skill category. Tis year, for the state and Seattle-King County , IT made up 66 percent of categorized skills. Five other skill categories, from Arts and Entertainment to Sales and Marketing and Maintenance made it into the top skill categories. Skills such as Online Advertising, Digital marketing and Athletic training made it into the top skill lists.

Te top six skills at the state level: Athletic training, Eloqua, Marketo, Google Ads, Google Analytics and Backpack blowers.

In Seattle-King County the top six skills: Athletic training, Group counseling, Eloqua, Google Docs, Marketo and Google Ads.


Skill projections Appendix 5

Te top 20 detailed skills for Washington state based on a combined (average) rank of average annual openings and growth rates for 2018 to 2028 are presented in Appendix fgure A5-1.

Appendix figure A5-1. Top 20 skills ranked by combined average annual openings and growth Washington state, 2018 to 2028 Source: Employment Security Department/LMEA; The Conference Board, Help Wanted OnLine job announcements

Combined rank Hard skill titles

Estimated hard skill

employment numbers

2018

Projected hard skill

employment numbers

2028

Average annual growth rate 2018-2028

Total average annual

openings 1 Athletic training 1,830 2,635 3.71% 924 2 Microsoft Active Directory 3,054 4,074 2.92% 1,248 3 Linux 6,817 8,757 2.54% 2,500 4 Firewall 2,995 3,954 2.82% 1,114 5 VMware 2,602 3,464 2.90% 1,005 6 Transmission Control Protocol 2,757 3,651 2.85% 1,064 7 Network routers 2,859 3,773 2.81% 1,073 8 Local Area Network 2,564 3,403 2.87% 996 9 Wide Area Network 2,734 3,619 2.84% 1,021 10 Virtualization 2,867 3,756 2.74% 1,097 10 Digital marketing 5,128 6,549 2.48% 2,246 12 Windows servers 2,360 3,144 2.91% 909 13 Cloud Computing 5,928 7,523 2.41% 2,344 14 Amazon Web Services 2,906 3,752 2.59% 1,070 14 Web services 5,173 6,548 2.38% 2,096 16 Structured query language 17,019 21,123 2.18% 6,332 17 Tableau Software 6,315 7,920 2.29% 2,429 18 Virtual Private Network 2,053 2,695 2.76% 792 19 Marketing communications 1,372 1,838 2.96% 645 20 Search Engine Optimization 1,852 2,418 2.70% 820

A non-information technology skill ranked number 1 in the list of top ranks for openings and growth.



Te top 20 occupations represent 3.73 percent of total openings in the skills forecast.

Information technology IT skills naturally dominate shares in computer-related occupations, but also have a very high share in occupations whose primary occupational focus is not computers. Te top 15 occupations with high computer skill requirements based on IT shares, are presented in Appendix fgure A5-2. Management Analysts and Operations Research Analysts were in both the 2019 and 2020 table. All other occupations are new to the top 15 this year.

Appendix figure A5-2. Occupations, not primarily computer related, with the largest shares of computer skill requirements Washington state, 2019 second quarter occupational estimations (June 2016 to June 2019 sample, skills/occupations matrices) Source: Employment Security Department/LMEA; The Conference Board, Help Wanted OnLine job announcements

SOC Occupation Share of skills that are IT 396012 Concierges 0.915 152021 Mathematicians 0.842 435053 Postal Service Mail Sorters, Processors, and Processing Machine Operators 0.821 192012 Physicists 0.810 194041 Geological and Petroleum Technicians 0.800 393092 Costume Attendants 0.792 271023 Floral Designers 0.777 152031 Operations Research Analysts 0.729 271026 Merchandise Displayers and Window Trimmers 0.729 271029 Designers, All Other 0.722 439051 Mail Clerks and Mail Machine Operators, Except Postal Service 0.721 251032 Engineering Teachers, Postsecondary 0.705 171011 Architects, Except Landscape and Naval 0.693 191032 Foresters 0.686 131111 Management Analysts 0.672

Only two of the current 15 occupations is the same as in last year’s report.

Skill based related occupations Skills–to-occupations matrices allow us to create a tool for defning related occupations, based on common skills. To achieve this, we calculated a matrix of correlations based on skills between occupations. Te results are presented in the macro-enabled fle, reloccup_skills_2019. xlsm. Te matrix in the fle’s “main” tab is symmetric around the main diagonal. Te main diagonal has all 1s in it. Tere are two ways of using the fle’s data when opened with the enabled-macros feature:


Skill projections Appendix 5

1. You can select an occupational title of interest, from a column heading, in the “main” tab and then sort the numbers below the title of interest from largest to smallest. Starting from row three in column B you would see the sorted list of related occupations (row two will be the same occupation as selected). To restore the original sort-confguration, sort the key-column (column A) from smallest to largest.

2. You can select an occupation of interest, from a column heading, in the “main” tab and then click the Ctrl and A keys simultaneously. Tis will execute a macro. Te macro opens a table in a “table” tab. In the table, you will fnd a list of the top 15 occupations related to your occupation of interest.

An example of a list for sofware developers, applications is in Appendix fgure A5-3.

Appendix figure A5-3. Top 15 occupations related to purchasing managers Washington state, 2020 Source: Employment Security Department/LMEA; The Conference Board, Help Wanted OnLine job announcements

SOC 151132-Software Developers, Applications 131081-Logisticians 0.749 152051-Data Scientists 0.729 113071-Transportation, Storage, and Distribution Managers 0.689 111021-General and Operations Managers 0.677 119199-Managers, All Other 0.645 131199-Business Operations Specialists, All Other 0.544 439051-Mail Clerks and Mail Machine Operators, Except Postal Service 0.508 439021-Data Entry Keyers 0.500 439081-Proofreaders and Copy Markers 0.497 433051-Payroll and Timekeeping Clerks 0.482 131121-Meeting, Convention, and Event Planners 0.480 435041-Meter Readers, Utilities 0.475 131151-Training and Development Specialists 0.470 151211-Computer Systems Analysts 0.470 119041-Architectural and Engineering Managers 0.443

Numbers in the table represent coefficients of correlations for normalized vectors of skill shares.

Te related occupations tool may be useful for job seekers. Te results are specifc for Washington state since the skills come from job announcements in this state.



Conclusions Our view is that it is more important to connect education and training programs with real world skill requirements than with generic occupational skills defnitions.

While primary felds are relatively stable and well defned, IT skill sets are constantly changing. IT skills are concentrated mainly in sofware, algorithms, some hardware and in web applications.

Some specifc skills, like those in Appendix fgure A5-1, are important and help graduates enter the labor market or move to higher paid jobs. However, in the long run, it might be worth giving priority to foundational academic subjects like math and formal logic, multidimensional design, and foundational concepts in object-oriented programing. In other words, foundational abilities to learn, develop and implement new knowledge and technology in the long run should take priority for career preparation.

Future possibilities Skill forecasts continue to be in an experimental phase. Improvements in skill extraction and clustering techniques would allow us to improve our skills products. As always, it will also continue to be important to establish a direct connection between specifc skills required by employers and education and training programs.

Appendix 6: Frequently asked questions Q: What are the steps in industry projections?

A: Tere are two major steps in industry projections. Te frst step is developing aggregated statewide industry projections using Global Insight national forecasts. Te second step produces detailed industry projections. Te principal data source for industry projections is a detailed covered employment time series of four-digit NAICS data for all Washington counties, specifcally, the U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages (QCEW).

Q: Why are the detailed industry projections not comparable with U.S. Bureau of Labor Statistics, Current Employment Statistics (CES) defnitions?

A: Industry projections are classifed according to U.S. Bureau of Labor Statistics, Occupational Employment Statistics (OES) defnitions, which are somewhat diferent from CES.

Q: What is the source for occupational/industry ratios?

A: Te primary source for occupational/industry ratios is the OES survey. However, this survey uses diferent area designations than the state’s workforce development areas (WDAs) and has limited industry coverage (agriculture, non-covered employment, private households and self-employment are excluded) necessitating the use of other stafng patterns as well.

Q: Why can the ratio for industry and occupational projections difer from the OES survey outputs?

A: We use raw sample and limited numbers of imputations while standard OES processing using signifcant share of imputations. We also use extra information from WEB job announcements. In cases when sample is weak or missing, we use substituted area (state stafng patterns) or combined areas (King and Snohomish counties).

Q: Why can occupational/industry ratios difer between the base year and projected years?

A: Tis is due to the use of change factors, which predict changes in the occupational shares for each industry over time.

Q: Why can’t occupational projections be benchmarked or verifed?

A: Tere are no administrative records for employment by occupation; therefore, the data cannot be reliably benchmarked or verifed by non-survey means.



Appendix 6 Frequently asked questions

Q: How are occupational projections used?

A: Occupational projections are the only data source for statewide and WDA-specifc occupational outlooks. Projections are also the foundation for developing the Occupations in Demand list, which is used to determine eligibility for a variety of training and support programs, but was created to support the unemployment insurance Training Benefts Program.

Q: How are industry projections used?

A: Industry projections can be used by policy makers, job seekers, job counselors and economic analysts. For any policy decisions, the projections should be supplemented with other available data sources (e.g., unemployment insurance claims, educational data, job announcements, etc.).

Q: Which occupational codes are used?

A: Te 2010 Standard Occupational Classifcation (SOC) system was used for this round of projections.

Q: Can the SOC be used for administrative purposes?

A: According to BLS, the 2010 SOC was designed solely for statistical purposes. To use SOC for administrative programs, the head of an agency considering using SOC must frst determine if the use of SOC defnitions is appropriate for a program’s objectives.

Q: Why don’t the occupational totals by WDA equal the state total?

A: Te totals are not additive due to the use of local stafng patterns for projections by WDA, which difer from the statewide stafng pattern.

Q: What is the diference between the Bureau of Labor Statistics separations rate and alternative state specifc rate methodologies?

A: Te separations method measures job openings created by workers who leave occupations and need to be replaced by new entrants. In this method, workers who exit the labor force or transfer to an occupation with a diferent Standard Occupational Classifcation (SOC) are identifed as generating separations openings at the national level. Tis means that jobs flled by workers within the same occupations, are not identifed as new jobs.

Te alternative rates track openings created by turnover within occupations (i.e., workers stay within occupations but transfer to diferent companies) and when workers leave one occupation for another or leave the workforce. In contrast to separation methodology, alternative openings represent total job openings and are specifc for Washington state.

Appendix 7: Glossary of terms Industries

A classifcation of business establishments based on similar production processes.

North American Industry Classifcation System (NAICS)

North American Industry Classifcation System (NAICS) is the system used by federal statistical agencies in classifying business establishments for the purpose of collecting, analyzing and publishing statistical data related to the U.S. business economy. NAICS was developed under the authority of the U.S. Ofce of Management and Budget.

Occupation

A job or profession, a category of jobs that are similar with respect to the work performed and the skills possessed by the workers.

Occupational projections

Industry projections converted to occupations, based on occupational/ industry ratios.

Standard Occupational Codes (SOC)

Standard Occupational Classifcation (SOC) is the system used by federal statistical agencies in classifying workers into occupational categories for the purpose of collecting, calculating or disseminating data. All workers are classifed into their occupational defnitions which are structured at four levels of aggregation. SOC was developed under the authority of the U.S. Ofce of Management and Budget.

Total occupational estimations and projections

Total occupational estimations and projections are calculated to describe employment in base years and future time periods.


2020 Labor Market and Economic Report - Wa

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